In [1]:
######## snakemake preamble start (automatically inserted, do not edit) ########
import sys; sys.path.extend(['/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/site-packages', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024', '/home/ckikawa/.conda/envs/seqneut-pipeline/bin', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/lib-dynload', '/home/ckikawa/.local/lib/python3.11/site-packages', '/home/ckikawa/.conda/envs/seqneut-pipeline/lib/python3.11/site-packages', '/home/ckikawa/.cache/snakemake/snakemake/source-cache/runtime-cache/tmpy6xj203u/file/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline/notebooks', '/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/seqneut-pipeline/notebooks']); import pickle; snakemake = 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Sequencing-based neutralization assays of 2023-2024 human serum samples versus H3N2 influenza libraries\n\nThe numerical data and computer code are at 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from snakemake.logging import logger; logger.printshellcmds = False; import os; os.chdir(r'/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024');
######## snakemake preamble end #########

Process plate counts to get fraction infectivities and fit curves¶

This notebook is designed to be run using snakemake, and analyzes a plate of sequencing-based neutralization assays.

The plots generated by this notebook are interactive, so you can mouseover points for details, use the mouse-scroll to zoom and pan, and use interactive dropdowns at the bottom of the plots.

Setup¶

Import Python modules:

In [2]:
import pickle
import sys

import altair as alt

import matplotlib.pyplot as plt

import neutcurve

import numpy

import pandas as pd

import ruamel.yaml as yaml

_ = alt.data_transformers.disable_max_rows()

Get the variables passed by snakemake:

In [3]:
count_csvs = snakemake.input.count_csvs
fate_csvs = snakemake.input.fate_csvs
viral_library_csv = snakemake.input.viral_library_csv
neut_standard_set_csv = snakemake.input.neut_standard_set_csv
qc_drops_yaml = snakemake.output.qc_drops
frac_infectivity_csv = snakemake.output.frac_infectivity_csv
fits_csv = snakemake.output.fits_csv
fits_pickle = snakemake.output.fits_pickle
samples = snakemake.params.samples
plate = snakemake.wildcards.plate
plate_params = snakemake.params.plate_params

# get thresholds turning lists into tuples as needed
manual_drops = {
    filter_type: [tuple(w) if isinstance(w, list) else w for w in filter_drops]
    for (filter_type, filter_drops) in plate_params["manual_drops"].items()
}
group = plate_params["group"]
qc_thresholds = plate_params["qc_thresholds"]
curvefit_params = plate_params["curvefit_params"]
curvefit_qc = plate_params["curvefit_qc"]
curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"] = [
    tuple(w) for w in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
]

print(f"Processing {plate=}")

samples_df = pd.DataFrame(plate_params["samples"])
print(f"\nPlate has {len(samples)} samples (wells)")
assert all(
    (len(samples_df) == samples_df[c].nunique())
    for c in ["well", "sample", "sample_noplate"]
)
assert len(samples_df) == len(
    samples_df.groupby(["serum_replicate", "dilution_factor"])
)
assert len(samples) == len(count_csvs) == len(fate_csvs) == len(samples_df)

for d, key, title in [
    (manual_drops, "manual_drops", "Data manually specified to drop:"),
    (qc_thresholds, "qc_thresholds", "QC thresholds applied to data:"),
    (curvefit_params, "curvefit_params", "Curve-fitting parameters:"),
    (curvefit_qc, "curvefit_qc", "Curve-fitting QC:"),
]:
    print(f"\n{title}")
    yaml.YAML(typ="rt").dump({key: d}, stream=sys.stdout)
Processing plate='plate6'

Plate has 24 samples (wells)

Data manually specified to drop:
manual_drops: {}
QC thresholds applied to data:
qc_thresholds:
  avg_barcode_counts_per_well: 500
  min_neut_standard_frac_per_well: 0.005
  no_serum_per_viral_barcode_filters:
    min_frac: 0.0001
    max_fold_change: 4
    max_wells: 2
  per_neut_standard_barcode_filters:
    min_frac: 0.005
    max_fold_change: 4
    max_wells: 2
  min_neut_standard_count_per_well: 1000
  min_no_serum_count_per_viral_barcode_well: 100
  max_frac_infectivity_per_viral_barcode_well: 3
  min_dilutions_per_barcode_serum_replicate: 6
Curve-fitting parameters:
curvefit_params:
  frac_infectivity_ceiling: 1
  fixtop:
  - 0.6
  - 1
  fixbottom: 0
  fixslope:
  - 0.8
  - 10
Curve-fitting QC:
curvefit_qc:
  max_frac_infectivity_at_least: 0.0
  goodness_of_fit:
    min_R2: 0.5
    max_RMSD: 0.15
  serum_replicates_ignore_curvefit_qc: []
  barcode_serum_replicates_ignore_curvefit_qc: []

Set up dictionary to keep track of wells, barcodes, well-barcodes, and serum-replicates that are dropped:

In [4]:
qc_drops = {
    "wells": {},
    "barcodes": {},
    "barcode_wells": {},
    "barcode_serum_replicates": {},
    "serum_replicates": {},
}

assert set(manual_drops).issubset(
    qc_drops
), f"{manual_drops.keys()=}, {qc_drops.keys()}"

Statistics on barcode-parsing for each sample¶

Make interactive chart of the "fates" of the sequencing reads parsed for each sample on the plate.

If most sequencing reads are not "valid barcodes", this could potentially indicate some problem in the sequencing or barcode set you are parsing.

Potential fates are:

  • valid barcode: barcode that matches a known virus or neutralization standard, we hope most reads are this.
  • invalid barcode: a barcode with proper flanking sequences, but does not match a known virus or neutralization standard. If you have a lot of reads of this type, it is probably a good idea to look at the invalid barcode CSVs (in the ./results/barcode_invalid/ subdirectory created by the pipeline) to see what these invalid barcodes are.
  • unparseable barcode: could not parse a barcode from this read as there was not a sequence of the correct length with the appropriate flanking sequence.
  • invalid outer flank: if using an outer upstream or downstream region (upstream2 or downstream2 for the illuminabarcodeparser), reads that are otherwise valid except for this outer flank. Typically you would be using upstream2 if you have a plate index embedded in your primer, and reads with this classification correspond to a different index than the one for this plate.
  • low quality barcode: low-quality or N nucleotides in barcode, could indicate problem with sequencing.
  • failed chastity filter: reads that failed the Illumina chastity filter, if these are reported in the FASTQ (they may not be).

Also, if the number of reads per sample is very uneven, that could indicate that you did not do a good job of balancing the different samples in the Illumina sequencing.

In [5]:
fates = (
    pd.concat([pd.read_csv(f).assign(sample=s) for f, s in zip(fate_csvs, samples)])
    .merge(samples_df, validate="many_to_one", on="sample")
    .assign(
        fate_counts=lambda x: x.groupby("fate")["count"].transform("sum"),
        sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")",
    )
    .query("fate_counts > 0")[  # only keep fates with at least one count
        ["fate", "count", "well", "serum_replicate", "sample_well", "dilution_factor"]
    ]
)

assert len(fates) == len(fates.drop_duplicates())

serum_replicates = sorted(fates["serum_replicate"].unique())
sample_wells = list(
    fates.sort_values(["serum_replicate", "dilution_factor"])["sample_well"]
)


serum_selection = alt.selection_point(
    fields=["serum_replicate"],
    bind=alt.binding_select(
        options=[None] + serum_replicates,
        labels=["all"] + serum_replicates,
        name="serum",
    ),
)

fates_chart = (
    alt.Chart(fates)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X("count", scale=alt.Scale(nice=False, padding=3)),
        alt.Y(
            "sample_well",
            title=None,
            sort=sample_wells,
        ),
        alt.Color("fate", sort=sorted(fates["fate"].unique(), reverse=True)),
        alt.Order("fate", sort="descending"),
        tooltip=fates.columns.tolist(),
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=200,
        title=f"Barcode parsing for {plate}",
    )
    .configure_axis(grid=False)
)

fates_chart
Out[5]:

Read barcode counts and apply manually specified drops¶

Read the counts per barcode:

In [6]:
# get barcode counts
counts = (
    pd.concat([pd.read_csv(c).assign(sample=s) for c, s in zip(count_csvs, samples)])
    .merge(samples_df, validate="many_to_one", on="sample")
    .drop(columns=["replicate", "plate", "fastq"])
    .assign(sample_well=lambda x: x["sample_noplate"] + " (" + x["well"] + ")")
)

# classify barcodes as viral or neut standard
barcode_class = pd.concat(
    [
        pd.read_csv(viral_library_csv)[["barcode", "strain"]].assign(
            neut_standard=False,
        ),
        pd.read_csv(neut_standard_set_csv)[["barcode"]].assign(
            neut_standard=True,
            strain=pd.NA,
        ),
    ],
    ignore_index=True,
)

# merge counts and classification of barcodes
assert set(counts["barcode"]) == set(barcode_class["barcode"])
counts = counts.merge(barcode_class, on="barcode", validate="many_to_one")
assert set(sample_wells) == set(counts["sample_well"])
assert set(serum_replicates) == set(counts["serum_replicate"])

Apply any manually specified data drops:

In [7]:
for filter_type, filter_drops in manual_drops.items():
    print(f"\nDropping {len(filter_drops)} {filter_type} specified in manual_drops")
    assert filter_type in qc_drops
    qc_drops[filter_type].update(
        {w: "manual_drop" for w in filter_drops if not isinstance(w, list)}
    )
    if filter_type == "barcode_wells":
        counts = counts[
            ~counts.assign(
                barcode_well=lambda x: x.apply(
                    lambda r: (r["barcode"], r["well"]), axis=1
                )
            )["barcode_well"].isin(qc_drops[filter_type])
        ]
    elif filter_type == "barcode_serum_replicates":
        counts = counts[
            ~counts.assign(
                barcode_serum_replicate=lambda x: x.apply(
                    lambda r: (r["barcode"], r["serum_replicate"]), axis=1
                )
            )["barcode_serum_replicate"].isin(qc_drops[filter_type])
        ]
    elif filter_type == "wells":
        counts = counts[~counts["well"].isin(qc_drops[filter_type])]
    elif filter_type == "barcodes":
        counts = counts[~counts["barcode"].isin(qc_drops[filter_type])]
    else:
        assert filter_type in set(counts.columns)
        counts = counts[~counts[filter_type].isin(qc_drops[filter_type])]

Average counts per barcode in each well¶

Plot average counts per barcode. If a sample has inadequate barcode counts, it may not have good enough statistics for accurate analysis, and a QC-threshold is applied:

In [8]:
avg_barcode_counts = (
    counts.groupby(
        ["well", "serum_replicate", "sample_well"],
        dropna=False,
        as_index=False,
    )
    .aggregate(avg_count=pd.NamedAgg("count", "mean"))
    .assign(
        fails_qc=lambda x: (
            x["avg_count"] < qc_thresholds["avg_barcode_counts_per_well"]
        ),
    )
)

avg_barcode_counts_chart = (
    alt.Chart(avg_barcode_counts)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "avg_count",
            title="average barcode counts per well",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['avg_barcode_counts_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            alt.Tooltip(c, format=".3g") if avg_barcode_counts[c].dtype == float else c
            for c in avg_barcode_counts.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Average barcode counts per well for {plate}",
    )
    .configure_axis(grid=False)
)

display(avg_barcode_counts_chart)

# drop wells failing QC
avg_barcode_counts_per_well_drops = list(avg_barcode_counts.query("fails_qc")["well"])
print(
    f"\nDropping {len(avg_barcode_counts_per_well_drops)} wells for failing "
    f"{qc_thresholds['avg_barcode_counts_per_well']=}: "
    + str(avg_barcode_counts_per_well_drops)
)
qc_drops["wells"].update(
    {w: "avg_barcode_counts_per_well" for w in avg_barcode_counts_per_well_drops}
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]
Dropping 0 wells for failing qc_thresholds['avg_barcode_counts_per_well']=500: []

Fraction of counts from neutralization standard¶

Determine the fraction of counts from the neutralization standard in each sample, and make sure this fraction passess the QC threshold.

In [9]:
neut_standard_fracs = (
    counts.assign(
        neut_standard_count=lambda x: x["count"] * x["neut_standard"].astype(int)
    )
    .groupby(
        ["well", "serum_replicate", "sample_well"],
        dropna=False,
        as_index=False,
    )
    .aggregate(
        total_count=pd.NamedAgg("count", "sum"),
        neut_standard_count=pd.NamedAgg("neut_standard_count", "sum"),
    )
    .assign(
        neut_standard_frac=lambda x: x["neut_standard_count"] / x["total_count"],
        fails_qc=lambda x: (
            x["neut_standard_frac"] < qc_thresholds["min_neut_standard_frac_per_well"]
        ),
    )
)

neut_standard_fracs_chart = (
    alt.Chart(neut_standard_fracs)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "neut_standard_frac",
            title="frac counts from neutralization standard per well",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['min_neut_standard_frac_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            alt.Tooltip(c, format=".3g") if neut_standard_fracs[c].dtype == float else c
            for c in neut_standard_fracs.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Neutralization-standard fracs per well for {plate}",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(neut_standard_fracs_chart)

# drop wells failing QC
min_neut_standard_frac_per_well_drops = list(
    neut_standard_fracs.query("fails_qc")["well"]
)
print(
    f"\nDropping {len(min_neut_standard_frac_per_well_drops)} wells for failing "
    f"{qc_thresholds['min_neut_standard_frac_per_well']=}: "
    + str(min_neut_standard_frac_per_well_drops)
)
qc_drops["wells"].update(
    {
        w: "min_neut_standard_frac_per_well"
        for w in min_neut_standard_frac_per_well_drops
    }
)
counts = counts[~counts["well"].isin(qc_drops["wells"])]
Dropping 0 wells for failing qc_thresholds['min_neut_standard_frac_per_well']=0.005: []

Consistency and minimum fractions for barcodes¶

We examine the fraction of counts attributable to each barcode. We do this splitting the data two ways:

  1. Looking at all viral (but not neut-standard) barcodes only for the no-serum samples (wells).

  2. Looking at just the neut-standard barcodes for all samples (wells).

The reasons is that if the experiment is set up perfectly, these fractions should be the same across all samples for each barcode. (We do not expect viral barcodes to have consistent fractions across no-serum samples as they will be neutralized differently depending on strain).

We plot these fractions in interactive plots (you can mouseover points and zoom) so you can identify barcodes that fail the expected consistency QC thresholds.

We also make sure the barcodes meet specified QC minimum thresholds for all samples, and flag any that do not.

In [10]:
barcode_selection = alt.selection_point(fields=["barcode"], on="mouseover", empty=False)

# look at all samples for neut standard barcodes, or no-serum samples for all barcodes
for is_neut_standard, df in counts.groupby("neut_standard"):
    if is_neut_standard:
        print(
            f"\n\n{'=' * 89}\nAnalyzing neut-standard barcodes from all samples (wells)"
        )
        qc_name = "per_neut_standard_barcode_filters"
    else:
        print(f"\n\n{'=' * 89}\nAnalyzing all barcodes from no-serum samples (wells)")
        qc_name = "no_serum_per_viral_barcode_filters"
        df = df.query("serum == 'none'")

    df = df.assign(
        sample_counts=lambda x: x.groupby("sample")["count"].transform("sum"),
        count_frac=lambda x: x["count"] / x["sample_counts"],
        median_count_frac=lambda x: x.groupby("barcode")["count_frac"].transform(
            "median"
        ),
        fold_change_from_median=lambda x: numpy.where(
            x["count_frac"] > x["median_count_frac"],
            x["count_frac"] / x["median_count_frac"],
            x["median_count_frac"] / x["count_frac"],
        ),
    )[
        [
            "barcode",
            "count",
            "well",
            "sample_well",
            "count_frac",
            "median_count_frac",
            "fold_change_from_median",
        ]
        + ([] if is_neut_standard else ["strain"])
    ]

    # barcode fails QC if fails in sufficient wells
    qc = qc_thresholds[qc_name]
    print(f"Apply QC {qc_name}: {qc}\n")
    fails_qc = (
        df.assign(
            fails_qc=lambda x: ~(
                (x["count_frac"] >= qc["min_frac"])
                & (x["fold_change_from_median"] <= qc["max_fold_change"])
            ),
        )
        .groupby("barcode", as_index=False)
        .aggregate(n_wells_fail_qc=pd.NamedAgg("fails_qc", "sum"))
        .assign(fails_qc=lambda x: x["n_wells_fail_qc"] >= qc["max_wells"])[
            ["barcode", "fails_qc"]
        ]
    )
    df = df.merge(fails_qc, on="barcode", validate="many_to_one")

    # make chart
    evenness_chart = (
        alt.Chart(df)
        .add_params(barcode_selection)
        .encode(
            alt.X(
                "count_frac",
                title=(
                    "barcode's fraction of neut standard counts"
                    if is_neut_standard
                    else "barcode's fraction of non-neut standard counts"
                ),
                scale=alt.Scale(nice=False, padding=5),
            ),
            alt.Y("sample_well", sort=sample_wells),
            alt.Fill(
                "fails_qc",
                title=f"fails {qc_name}",
                legend=alt.Legend(titleLimit=500),
            ),
            strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
            size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
            tooltip=[
                alt.Tooltip(c, format=".2g") if df[c].dtype == float else c
                for c in df.columns
            ],
        )
        .mark_circle(fillOpacity=0.45, stroke="black", strokeOpacity=1)
        .properties(
            height=alt.Step(10),
            width=300,
            title=alt.TitleParams(
                (
                    f"{plate} all samples, neut-standard barcodes"
                    if is_neut_standard
                    else f"{plate} no-serum samples, all barcodes"
                ),
                subtitle="x-axis is zoomable (use mouse scroll/pan)",
            ),
        )
        .configure_axis(grid=False)
        .configure_legend(titleLimit=1000)
        .interactive()
    )

    display(evenness_chart)

    # drop barcodes failing QC
    barcode_drops = list(fails_qc.query("fails_qc")["barcode"])
    print(
        f"\nDropping {len(barcode_drops)} barcodes for failing {qc=}: {barcode_drops}"
    )
    qc_drops["barcodes"].update(
        {bc: "min_neut_standard_frac_per_well" for bc in barcode_drops}
    )
    counts = counts[~counts["barcode"].isin(qc_drops["barcodes"])]

=========================================================================================
Analyzing all barcodes from no-serum samples (wells)
Apply QC no_serum_per_viral_barcode_filters: {'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}

Dropping 17 barcodes for failing qc={'min_frac': 0.0001, 'max_fold_change': 4, 'max_wells': 2}: ['AAAGTAGCAGAGGATT', 'AAATTCACAATATCCA', 'AGACCATCGCACCCAA', 'ATAACGTTTGTGCAAA', 'CAAAAGCAGCACGATA', 'CACCGACCAACTCTCT', 'CATAAAAGACTGTATA', 'CCAGAGACACGCTAGG', 'CCCTCCTCAAGGGTAA', 'CCTATAAGGCCTTACG', 'CGTACGTATGTCCCAG', 'CGTCCCTGGCGTGTCG', 'CGTTAACGGCCTATCC', 'CTCCAATAGGAGACGA', 'TATATGGAATACTAAA', 'TCTCCGATAGCCCTAC', 'TGTTGTAATCTGAATA']


=========================================================================================
Analyzing neut-standard barcodes from all samples (wells)
Apply QC per_neut_standard_barcode_filters: {'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}

Dropping 0 barcodes for failing qc={'min_frac': 0.005, 'max_fold_change': 4, 'max_wells': 2}: []

Compute fraction infectivity¶

The fraction infectivity for viral barcode $v_b$ in sample $s$ is computed as: $$ F_{v_b,s} = \frac{c_{v_b,s} / \left(\sum_{n_b} c_{n_b,s}\right)}{{\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]} $$ where

  • $c_{v_b,s}$ is the counts of viral barcode $v_b$ in sample $s$.
  • $\sum_{n_b} c_{n_b,s}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for sample $s$.
  • $c_{v_b,s_0}$ is the counts of viral barcode $v_b$ in no-serum sample $s_0$.
  • $\sum_{n_b} c_{n_b,s_0}$ is the sum of the counts for all neutralization standard barcodes $n_b$ for no-serum sample $s_0$.
  • ${\rm median}_{s_0}\left[ c_{v_b,s_0} / \left(\sum_{n_b} c_{n_b,s_0}\right)\right]$ is the median taken across all no-serum samples of the counts of viral barcode $v_b$ versus the total counts for all neutralization standard barcodes.

First, compute the total neutralization-standard counts for each sample (well). Plot these, and drop any wells that do not meet the QC threshold.

In [11]:
neut_standard_counts = (
    counts.query("neut_standard")
    .groupby(
        ["well", "serum_replicate", "sample_well", "dilution_factor"],
        dropna=False,
        as_index=False,
    )
    .aggregate(neut_standard_count=pd.NamedAgg("count", "sum"))
    .assign(
        fails_qc=lambda x: (
            x["neut_standard_count"] < qc_thresholds["min_neut_standard_count_per_well"]
        ),
    )
)

neut_standard_counts_chart = (
    alt.Chart(neut_standard_counts)
    .add_params(serum_selection)
    .transform_filter(serum_selection)
    .encode(
        alt.X(
            "neut_standard_count",
            title="counts from neutralization standard",
            scale=alt.Scale(nice=False, padding=3),
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Color(
            "fails_qc",
            title=f"fails {qc_thresholds['min_neut_standard_count_per_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if neut_standard_counts[c].dtype == float
                else c
            )
            for c in neut_standard_counts.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(10),
        width=250,
        title=f"Neutralization-standard counts for {plate}",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(neut_standard_counts_chart)

# drop wells failing QC
min_neut_standard_count_per_well_drops = list(
    neut_standard_counts.query("fails_qc")["well"]
)
print(
    f"\nDropping {len(min_neut_standard_count_per_well_drops)} wells for failing "
    f"{qc_thresholds['min_neut_standard_count_per_well']=}: "
    + str(min_neut_standard_count_per_well_drops)
)
qc_drops["wells"].update(
    {
        w: "min_neut_standard_count_per_well"
        for w in min_neut_standard_count_per_well_drops
    }
)
neut_standard_counts = neut_standard_counts[
    ~neut_standard_counts["well"].isin(qc_drops["wells"])
]
counts = counts[~counts["well"].isin(qc_drops["wells"])]
Dropping 0 wells for failing qc_thresholds['min_neut_standard_count_per_well']=1000: []

Compute and plot the no-serum sample viral barcode counts and check if they pass the QC filters.

In [12]:
no_serum_counts = (
    counts.query("serum == 'none'")
    .query("not neut_standard")
    .merge(neut_standard_counts, validate="many_to_one")[
        ["barcode", "strain", "well", "sample_well", "count", "neut_standard_count"]
    ]
    .assign(
        fails_qc=lambda x: (
            x["count"] <= qc_thresholds["min_no_serum_count_per_viral_barcode_well"]
        ),
    )
)

strains = sorted(no_serum_counts["strain"].unique())
strain_selection_dropdown = alt.selection_point(
    fields=["strain"],
    bind=alt.binding_select(
        options=[None] + strains,
        labels=["all"] + strains,
        name="virus strain",
    ),
)

# make chart
no_serum_counts_chart = (
    alt.Chart(no_serum_counts)
    .add_params(barcode_selection, strain_selection_dropdown)
    .transform_filter(strain_selection_dropdown)
    .encode(
        alt.X(
            "count", title="viral barcode count", scale=alt.Scale(nice=False, padding=5)
        ),
        alt.Y("sample_well", sort=sample_wells),
        alt.Fill(
            "fails_qc",
            title=f"fails {qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}",
            legend=alt.Legend(titleLimit=500),
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
        size=alt.condition(barcode_selection, alt.value(60), alt.value(35)),
        tooltip=no_serum_counts.columns.tolist(),
    )
    .mark_circle(fillOpacity=0.6, stroke="black", strokeOpacity=1)
    .properties(
        height=alt.Step(10),
        width=400,
        title=f"{plate} viral barcode counts in no-serum samples",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
    .interactive()
)

display(no_serum_counts_chart)

# drop barcode / wells failing QC
min_no_serum_count_per_viral_barcode_well_drops = list(
    no_serum_counts.query("fails_qc")[["barcode", "well"]].itertuples(
        index=False, name=None
    )
)
print(
    f"\nDropping {len(min_no_serum_count_per_viral_barcode_well_drops)} barcode-wells for failing "
    f"{qc_thresholds['min_no_serum_count_per_viral_barcode_well']=}: "
    + str(min_no_serum_count_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
    {
        w: "min_no_serum_count_per_viral_barcode_well"
        for w in min_no_serum_count_per_viral_barcode_well_drops
    }
)
no_serum_counts = no_serum_counts[
    ~no_serum_counts.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]
counts = counts[
    ~counts.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]
Dropping 15 barcode-wells for failing qc_thresholds['min_no_serum_count_per_viral_barcode_well']=100: [('AAATGCTGAGAGGGTA', 'C12'), ('AGCAGACACTTTACAT', 'C12'), ('CAATTCGCCGTTCCCC', 'C12'), ('GTAGAAACTAGGAGTT', 'C12'), ('GTAATTCGCATGCGGA', 'C12'), ('CAAGACAAGCCCTATA', 'C12'), ('TCCCCGTGGTTTGACA', 'C12'), ('CCAATCCCAGCCTTTA', 'C12'), ('TTACATTTTTAGAATT', 'C12'), ('CTTCATCTCATTTAAA', 'G12'), ('TCCGCCACTATAACAT', 'G12'), ('CCGCTATCATTAACCC', 'G12'), ('ACAAAGATAAAAATTT', 'G12'), ('CACGTTAGTGAGACTT', 'G12'), ('ATGCGTCTAAACATAG', 'G12')]

Compute and plot the median ratio of viral barcode count to neut standard counts across no-serum samples. If library composition is equal, all of these values should be similar:

In [13]:
median_no_serum_ratio = (
    no_serum_counts.assign(ratio=lambda x: x["count"] / x["neut_standard_count"])
    .groupby(["barcode", "strain"], as_index=False)
    .aggregate(median_no_serum_ratio=pd.NamedAgg("ratio", "median"))
)

strain_selection = alt.selection_point(fields=["strain"], on="mouseover", empty=False)

median_no_serum_ratio_chart = (
    alt.Chart(median_no_serum_ratio)
    .add_params(strain_selection)
    .encode(
        alt.X(
            "median_no_serum_ratio",
            title="median ratio of counts",
            scale=alt.Scale(nice=False, padding=5),
        ),
        alt.Y(
            "barcode",
            sort=alt.SortField("median_no_serum_ratio", order="descending"),
            axis=alt.Axis(labelFontSize=5),
        ),
        color=alt.condition(strain_selection, alt.value("orange"), alt.value("gray")),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if median_no_serum_ratio[c].dtype == float
                else c
            )
            for c in median_no_serum_ratio.columns
        ],
    )
    .mark_bar(height={"band": 0.85})
    .properties(
        height=alt.Step(5),
        width=250,
        title=f"{plate} no-serum median ratio viral barcode to neut-standard barcode",
    )
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
)

display(median_no_serum_ratio_chart)

Compute the actual fraction infectivities. We compute both the raw fraction infectivities and the ones with the ceiling applied:

In [14]:
frac_infectivity = (
    counts.query("not neut_standard")
    .query("serum != 'none'")
    .merge(median_no_serum_ratio, validate="many_to_one")
    .merge(neut_standard_counts, validate="many_to_one")
    .assign(
        frac_infectivity_raw=lambda x: (
            (x["count"] / x["neut_standard_count"]) / x["median_no_serum_ratio"]
        ),
        frac_infectivity_ceiling=lambda x: x["frac_infectivity_raw"].clip(
            upper=curvefit_params["frac_infectivity_ceiling"]
        ),
        concentration=lambda x: 1 / x["dilution_factor"],
        plate_barcode=lambda x: x["plate_replicate"] + "-" + x["barcode"],
    )[
        [
            "barcode",
            "plate_barcode",
            "well",
            "strain",
            "serum",
            "serum_replicate",
            "dilution_factor",
            "concentration",
            "frac_infectivity_raw",
            "frac_infectivity_ceiling",
        ]
    ]
)

assert len(
    frac_infectivity.groupby(["serum", "plate_barcode", "dilution_factor"])
) == len(frac_infectivity)
assert frac_infectivity["dilution_factor"].notnull().all()
assert frac_infectivity["frac_infectivity_raw"].notnull().all()
assert frac_infectivity["frac_infectivity_ceiling"].notnull().all()

Plot the fraction infectivities, both the raw values and with the ceiling applied:

In [15]:
frac_infectivity_chart_df = (
    frac_infectivity.assign(
        fails_qc=lambda x: (
            x["frac_infectivity_raw"]
            > qc_thresholds["max_frac_infectivity_per_viral_barcode_well"]
        ),
    )
    .melt(
        id_vars=[
            "barcode",
            "strain",
            "well",
            "serum_replicate",
            "dilution_factor",
            "fails_qc",
        ],
        value_vars=["frac_infectivity_raw", "frac_infectivity_ceiling"],
        var_name="ceiling_applied",
        value_name="frac_infectivity",
    )
    .assign(
        ceiling_applied=lambda x: x["ceiling_applied"].map(
            {
                "frac_infectivity_raw": "raw fraction infectivity",
                "frac_infectivity_ceiling": f"fraction infectivity with ceiling at {curvefit_params['frac_infectivity_ceiling']}",
            }
        )
    )
)

frac_infectivity_chart = (
    alt.Chart(frac_infectivity_chart_df)
    .add_params(strain_selection_dropdown, barcode_selection)
    .transform_filter(strain_selection_dropdown)
    .encode(
        alt.X(
            "dilution_factor",
            title="dilution factor",
            scale=alt.Scale(nice=False, padding=5, type="log"),
        ),
        alt.Y(
            "frac_infectivity",
            title="fraction infectivity",
            scale=alt.Scale(nice=False, padding=5),
        ),
        alt.Column(
            "ceiling_applied",
            sort="descending",
            title=None,
            header=alt.Header(labelFontSize=13, labelFontStyle="bold", labelPadding=2),
        ),
        alt.Row(
            "serum_replicate",
            title=None,
            spacing=3,
            header=alt.Header(labelFontSize=13, labelFontStyle="bold"),
        ),
        alt.Detail("barcode"),
        alt.Shape(
            "fails_qc",
            title=f"fails {qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}",
            legend=alt.Legend(titleLimit=500, orient="bottom"),
        ),
        color=alt.condition(
            barcode_selection, alt.value("black"), alt.value("MediumBlue")
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(3), alt.value(1)),
        opacity=alt.condition(barcode_selection, alt.value(1), alt.value(0.25)),
        tooltip=[
            (
                alt.Tooltip(c, format=".3g")
                if frac_infectivity_chart_df[c].dtype == float
                else c
            )
            for c in frac_infectivity_chart_df.columns
        ],
    )
    .mark_line(point=True)
    .properties(
        height=150,
        width=250,
        title=f"Fraction infectivities for {plate}",
    )
    .interactive(bind_x=False)
    .configure_axis(grid=False)
    .configure_legend(titleLimit=1000)
    .configure_point(size=50)
    .resolve_scale(x="independent", y="independent")
)

display(frac_infectivity_chart)

# drop barcode / wells failing QC
max_frac_infectivity_per_viral_barcode_well_drops = list(
    frac_infectivity_chart_df.query("fails_qc")[["barcode", "well"]]
    .drop_duplicates()
    .itertuples(index=False, name=None)
)
print(
    f"\nDropping {len(max_frac_infectivity_per_viral_barcode_well_drops)} barcode-wells for failing "
    f"{qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=}: "
    + str(max_frac_infectivity_per_viral_barcode_well_drops)
)
qc_drops["barcode_wells"].update(
    {
        w: "max_frac_infectivity_per_viral_barcode_well"
        for w in max_frac_infectivity_per_viral_barcode_well_drops
    }
)
frac_infectivity = frac_infectivity[
    ~frac_infectivity.assign(
        barcode_well=lambda x: x.apply(lambda r: (r["barcode"], r["well"]), axis=1)
    )["barcode_well"].isin(qc_drops["barcode_wells"])
]
Dropping 197 barcode-wells for failing qc_thresholds['max_frac_infectivity_per_viral_barcode_well']=3: [('ATCGAAAAAACTGCAA', 'C2'), ('TCCCGAACTGAACGCG', 'C2'), ('CTGAAACCTTGTCCTA', 'C3'), ('TTAACCTAACGTATAG', 'C3'), ('TCGCGGTAGATTTGCG', 'C3'), ('ACAAAGATAAAAATTT', 'C3'), ('AAACTTCGTGGTATAC', 'C4'), ('AAGAAATTATGGCAGG', 'C4'), ('CGCAAGGGATACTAAC', 'C5'), ('TGGCTAGCGCACACCA', 'C5'), ('GCTGAAGACAGTATTA', 'C5'), ('AAAGATAAATTCAAAA', 'C5'), ('ATCGATTCGATTGACG', 'C6'), ('CGTCAGAAGTTTATAA', 'C6'), ('CCGGTTTTCGGGACCT', 'C6'), ('CAAGACAAGCCCTATA', 'C6'), ('CTCATTACAGAAATTG', 'C6'), ('AATCCGGTTAACCCCG', 'C6'), ('TGTAGTATAAGAATAA', 'C6'), ('GATTCACGGCCCACAA', 'C6'), ('CATAATGCACAAACGC', 'C6'), ('ACAAAGATAAAAATTT', 'C6'), ('ATCGAAAAAACTGCAA', 'C6'), ('GAAGTAACAAACTATG', 'C7'), ('AAATTTTTCTTTAGAC', 'C7'), ('CGCGAATCACTAAGTA', 'C7'), ('CGCAGTACACAACAAG', 'C7'), ('ACTGAACAGTATAACT', 'C7'), ('AATCCGATAAGAGCTA', 'C7'), ('TCAAACTATGATATTC', 'C7'), ('CTGAAACCTTGTCCTA', 'C7'), ('CAAGACAAGCCCTATA', 'C7'), ('CACCACAGTTACTTAA', 'C7'), ('AATCCGGTTAACCCCG', 'C7'), ('CCGATAAGACGTCGCT', 'C8'), ('CTGCGAATATTGTGAC', 'C8'), ('ATTAGATTATAACGTA', 'C8'), ('AGCTGAATTAAGTATG', 'C8'), ('TAGTTGCCCCGACCTG', 'C8'), ('TGTAGTATAAGAATAA', 'C8'), ('TACGAAAATCAAGAGC', 'C8'), ('ATCGAAAAAACTGCAA', 'C8'), ('ATCGTCCCGGACATTT', 'C8'), ('TCCACACCCCTAGCTA', 'C8'), ('TTTCATATAATTTGAG', 'C8'), ('AATCCGGTTAACCCCG', 'C8'), ('ACAAAGATAAAAATTT', 'C8'), ('TTGCAATTGAAACATA', 'C9'), ('AAACTTCGTGGTATAC', 'C9'), ('TAGTTGCCCCGACCTG', 'C9'), ('AAGTTGTACTTAAGGC', 'C9'), ('AATATACCGGCACTAC', 'C9'), ('CACCCAAACGTTCGCA', 'C9'), ('TGACAAACACCTGAGG', 'C9'), ('TCTTAACTACCCGATG', 'C9'), ('TGAATTGCGTGATGGG', 'C9'), ('AATCTTTCCAATCTTG', 'C9'), ('GGGTGCAATGAATCCA', 'C9'), ('TGTAGTATAAGAATAA', 'C9'), ('TGCCGATCCAATTGAT', 'C9'), ('TTTACTCGACTAACCG', 'C9'), ('TTTCATATAATTTGAG', 'C9'), ('TCCCGAACTGAACGCG', 'C9'), ('CCGCTATCATTAACCC', 'C9'), ('ACAAAGATAAAAATTT', 'C9'), ('CTGAAACCTTGTCCTA', 'C10'), ('CGTCAGAAGTTTATAA', 'C10'), ('AGACTGTACGCGACAG', 'C10'), ('AAGTGGTAGGATAAAA', 'C10'), ('TTATGTTTTAATGGTA', 'C10'), ('CGCGAATCACTAAGTA', 'C10'), ('ATCGAAAAAACTGCAA', 'C10'), ('ATAAAGAATCCCTTGA', 'C10'), ('ATCGTCCCGGACATTT', 'C10'), ('CCGCTATCATTAACCC', 'C10'), ('CACCACAGTTACTTAA', 'C10'), ('AATCCGGTTAACCCCG', 'C10'), ('ATTAGATTATAACGTA', 'C11'), ('TCAATGAATGCGGGGT', 'C11'), ('CTGAAACCTTGTCCTA', 'C11'), ('AAAGATAAATTCAAAA', 'C11'), ('CGCGAACAACAGGGGA', 'C11'), ('CCGGTTTTCGGGACCT', 'C11'), ('TATACTCACGGAGGAT', 'C11'), ('AAATGCTGAGAGGGTA', 'C11'), ('AGTGCTATAAAAATCA', 'C11'), ('ATAAAGAATCCCTTGA', 'C11'), ('CGGCTAAAGTCTATAG', 'C11'), ('AACGACAACCATGAAT', 'C11'), ('AATTACGCATAGGCCA', 'C11'), ('CGCAGTACACAACAAG', 'C11'), ('TTTCATATAATTTGAG', 'C11'), ('ATCGAAAAAACTGCAA', 'C11'), ('AGAAAAACGACATCAT', 'C11'), ('AATCCGGTTAACCCCG', 'C11'), ('GATTCACGGCCCACAA', 'C11'), ('CACCACAGTTACTTAA', 'C11'), ('CGCCGAACGGCGGCGC', 'C11'), ('GTAGAAACTAGGAGTT', 'C11'), ('CCATCACCTTATACAC', 'G2'), ('AAGTTGTACTTAAGGC', 'G3'), ('ACAGTACAGATATGAC', 'G4'), ('AATCTTTCCAATCTTG', 'G4'), ('AAGTTGTACTTAAGGC', 'G4'), ('ACAAAGATAAAAATTT', 'G4'), ('TATCATTTCATCTACA', 'G5'), ('AATATACCGGCACTAC', 'G5'), ('CTGAAACCTTGTCCTA', 'G5'), ('CGTCAGAAGTTTATAA', 'G5'), ('CTCATTACAGAAATTG', 'G5'), ('AATTACGCATAGGCCA', 'G5'), ('CACCACAGTTACTTAA', 'G5'), ('CAAGACAAGCCCTATA', 'G5'), ('CTGAAACCTTGTCCTA', 'G6'), ('CGTATAACTGACGATT', 'G6'), ('AAGAAATTATGGCAGG', 'G6'), ('TGTAGTATAAGAATAA', 'G7'), ('CTGCGAATATTGTGAC', 'G7'), ('CTGAAACCTTGTCCTA', 'G7'), ('AATCTTTCCAATCTTG', 'G7'), ('CCGGTTTTCGGGACCT', 'G7'), ('TTTCATATAATTTGAG', 'G7'), ('ATCGAAAAAACTGCAA', 'G7'), ('CAAGACAAGCCCTATA', 'G7'), ('ACAAAGATAAAAATTT', 'G7'), ('TACGAAAATCAAGAGC', 'G8'), ('GCATTATAATCTTGTG', 'G8'), ('TTGACTCACCGAATAA', 'G8'), ('ATAATAATCATCAAGA', 'G8'), ('ACCGTTTTTCTACCAG', 'G8'), ('CGTCAGAAGTTTATAA', 'G8'), ('AGTGCTATAAAAATCA', 'G8'), ('AGCTATGCCTAGTGAA', 'G8'), ('ACCCTTTTAGATATGA', 'G8'), ('AATTACGCATAGGCCA', 'G8'), ('AATCCGGTTAACCCCG', 'G8'), ('CAATTCGCCGTTCCCC', 'G8'), ('CCGCTATCATTAACCC', 'G8'), ('CACCACAGTTACTTAA', 'G8'), ('ATGCGTCTAAACATAG', 'G8'), ('AATGACAGCTGTCTAG', 'G8'), ('TGATTCGTCAATTCAT', 'G9'), ('GACCAAAAAGCAGTAT', 'G9'), ('AGACTGTACGCGACAG', 'G9'), ('GACCAAAGCTGCAGGG', 'G9'), ('CTGAAACCTTGTCCTA', 'G9'), ('GGGTGCAATGAATCCA', 'G9'), ('CGCCGAACGGCGGCGC', 'G9'), ('CAAGACAAGCCCTATA', 'G9'), ('TCCCGAACTGAACGCG', 'G9'), ('TCTTACATTGAAAGGC', 'G10'), ('GCCCATTGAACGCAGC', 'G10'), ('CTGTGAAAAAAAATAC', 'G10'), ('GCTGGTGCACAAGATT', 'G10'), ('GGTTGCGTAGTTAATC', 'G10'), ('CGATCTTTACGAAAAA', 'G10'), ('AACAAGGCCAACATTT', 'G10'), ('TTAGCTACTAACCCGT', 'G10'), ('AGCAGACACTTTACAT', 'G10'), ('ACCCTTTTAGATATGA', 'G10'), ('AATCCGATAAGAGCTA', 'G10'), ('GAAGTAACAAACTATG', 'G10'), ('TAATAACTTGAGATTC', 'G10'), ('AAGTGGTAGGATAAAA', 'G10'), ('TTATAATGGCCGGTAT', 'G10'), ('CCCGCTAACCCTGTCT', 'G10'), ('CGCGAACAACAGGGGA', 'G10'), ('TGCCGATCCAATTGAT', 'G10'), ('CACCACAGTTACTTAA', 'G10'), ('CAAGACAAGCCCTATA', 'G10'), ('CCGCTATCATTAACCC', 'G10'), ('CGCCGAACGGCGGCGC', 'G10'), ('ATAAAGAATCCCTTGA', 'G11'), ('AATTACGCATAGGCCA', 'G11'), ('AACGACAACCATGAAT', 'G11'), ('AATAGGCCCAAATCCA', 'G11'), ('CCAATAAAATACGATG', 'G11'), ('TCAATGAATGCGGGGT', 'G11'), ('AACGACACTTACATCC', 'G11'), ('ATTAGATTATAACGTA', 'G11'), ('CGTCAGAAGTTTATAA', 'G11'), ('GACCTCCTGGGCACGC', 'G11'), ('AACAATTAATTTTTCA', 'G11'), ('CGATCTTTACGAAAAA', 'G11'), ('AATCCGATAAGAGCTA', 'G11'), ('CTGAAACCTTGTCCTA', 'G11'), ('CATAATGCACAAACGC', 'G11'), ('TCGCGGTAGATTTGCG', 'G11'), ('AACCACCCCAGAGATG', 'G11'), ('ACGGACAACCTATCGC', 'G11'), ('CCGCTATCATTAACCC', 'G11'), ('TCCACACCCCTAGCTA', 'G11'), ('ATCGAAAAAACTGCAA', 'G11'), ('TTACATTTTTAGAATT', 'G11'), ('CACCACAGTTACTTAA', 'G11'), ('CGCCGAACGGCGGCGC', 'G11'), ('ACAAAGATAAAAATTT', 'G11')]

Check how many dilutions we have per barcode / serum-replicate:

In [16]:
n_dilutions = (
    frac_infectivity.groupby(["serum_replicate", "strain", "barcode"], as_index=False)
    .aggregate(**{"number of dilutions": pd.NamedAgg("dilution_factor", "nunique")})
    .assign(
        fails_qc=lambda x: (
            x["number of dilutions"]
            < qc_thresholds["min_dilutions_per_barcode_serum_replicate"]
        ),
    )
)

n_dilutions_chart = (
    alt.Chart(n_dilutions)
    .add_params(barcode_selection)
    .encode(
        alt.X("number of dilutions", scale=alt.Scale(nice=False, padding=4)),
        alt.Y("strain", title=None),
        alt.Column(
            "serum_replicate",
            title=None,
            header=alt.Header(labelFontSize=12, labelFontStyle="bold", labelPadding=0),
        ),
        alt.Fill(
            "fails_qc",
            title=f"fails {qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}",
            legend=alt.Legend(titleLimit=500, orient="bottom"),
        ),
        strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
        size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
        tooltip=[
            alt.Tooltip(c, format=".3g") if n_dilutions[c].dtype == float else c
            for c in n_dilutions.columns
        ],
    )
    .mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.45)
    .properties(
        height=alt.Step(10),
        width=120,
        title=alt.TitleParams(
            "number of dilutions for each barcode for each serum-replicate", dy=-2
        ),
    )
)

display(n_dilutions_chart)

# drop barcode / serum-replicates failing QC
min_dilutions_per_barcode_serum_replicate_drops = list(
    n_dilutions.query("fails_qc")[["barcode", "serum_replicate"]].itertuples(
        index=False, name=None
    )
)
print(
    f"\nDropping {len(min_dilutions_per_barcode_serum_replicate_drops)} barcode/serum-replicates for failing "
    f"{qc_thresholds['min_dilutions_per_barcode_serum_replicate']=}: "
    + str(min_dilutions_per_barcode_serum_replicate_drops)
)
qc_drops["barcode_serum_replicates"].update(
    {
        w: "min_dilutions_per_barcode_serum_replicate"
        for w in min_dilutions_per_barcode_serum_replicate_drops
    }
)
frac_infectivity = frac_infectivity[
    ~frac_infectivity.assign(
        barcode_serum_replicate=lambda x: x.apply(
            lambda r: (r["barcode"], r["serum_replicate"]), axis=1
        )
    )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
]
Dropping 0 barcode/serum-replicates for failing qc_thresholds['min_dilutions_per_barcode_serum_replicate']=6: []

Fit neutralization curves without applying QC to curves¶

First fit curves to all serum replicates, then we will apply QC on the curve fits. Note that the fitting is done to the fraction infectivities with the ceiling:

In [17]:
fits_noqc = neutcurve.CurveFits(
    frac_infectivity.rename(
        columns={
            "frac_infectivity_ceiling": "fraction infectivity",
            "concentration": "serum concentration",
        }
    ),
    conc_col="serum concentration",
    fracinf_col="fraction infectivity",
    virus_col="strain",
    serum_col="serum_replicate",
    replicate_col="barcode",
    fixtop=curvefit_params["fixtop"],
    fixbottom=curvefit_params["fixbottom"],
    fixslope=curvefit_params["fixslope"],
)

Determine which fits fail the curve fitting QC, and plot them. Note the plot indicates as failing QC any barcode / serum-replicate that fails, even if we are also specified to ignore the QC for that one (so it will not be removed later):

In [18]:
goodness_of_fit = curvefit_qc["goodness_of_fit"]

fit_params_noqc = (
    frac_infectivity.groupby(["serum_replicate", "barcode"], as_index=False)
    .aggregate(max_frac_infectivity=pd.NamedAgg("frac_infectivity_ceiling", "max"))
    .merge(
        fits_noqc.fitParams(average_only=False, no_average=True)[
            ["serum", "virus", "replicate", "r2", "rmsd"]
        ].rename(columns={"serum": "serum_replicate", "replicate": "barcode"}),
        validate="one_to_one",
    )
    .assign(
        fails_max_frac_infectivity_at_least=lambda x: (
            x["max_frac_infectivity"] < curvefit_qc["max_frac_infectivity_at_least"]
        ),
        fails_goodness_of_fit=lambda x: (
            (x["r2"] < goodness_of_fit["min_R2"])
            & (x["rmsd"] > goodness_of_fit["max_RMSD"])
        ),
        fails_qc=lambda x: (
            x["fails_max_frac_infectivity_at_least"] | x["fails_goodness_of_fit"]
        ),
        ignore_qc=lambda x: x.apply(
            lambda r: (
                (
                    r["serum_replicate"]
                    in curvefit_qc["serum_replicates_ignore_curvefit_qc"]
                )
                or (
                    (r["barcode"], r["serum_replicate"])
                    in curvefit_qc["barcode_serum_replicates_ignore_curvefit_qc"]
                )
            ),
            axis=1,
        ),
    )
)

print(f"Plotting barcode / serum-replicates that fail {curvefit_qc=}\n")

for prop, col in [
    ("max frac infectivity", "max_frac_infectivity"),
    ("curve fit R2", "r2"),
    ("curve fit RMSD", "rmsd"),
]:
    fit_params_noqc_chart = (
        alt.Chart(fit_params_noqc)
        .add_params(barcode_selection)
        .encode(
            alt.X(col, title=prop, scale=alt.Scale(nice=False, padding=4)),
            alt.Y("virus", title=None),
            alt.Fill("fails_qc"),
            alt.Column(
                "serum_replicate",
                title=None,
                header=alt.Header(
                    labelFontSize=12, labelFontStyle="bold", labelPadding=0
                ),
            ),
            strokeWidth=alt.condition(barcode_selection, alt.value(2), alt.value(0)),
            size=alt.condition(barcode_selection, alt.value(55), alt.value(35)),
            tooltip=[
                alt.Tooltip(c, format=".3g") if fit_params_noqc[c].dtype == float else c
                for c in fit_params_noqc.columns
            ],
        )
        .mark_circle(stroke="black", strokeOpacity=1, fillOpacity=0.55)
        .properties(
            height=alt.Step(10),
            width=120,
            title=alt.TitleParams(f"{prop} for each barcode serum-replicate", dy=-2),
        )
    )
    display(fit_params_noqc_chart)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
Plotting barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}

Now get all barcode / serum-replicate pairs that fail any of the QC. Plot curves for just these virus / serum-replicates (we plot all barcodes for a virus even if just one fails QC), and then exclude any that are not specified to ignore the QC:

In [19]:
barcode_serum_replicates_fail_qc = fit_params_noqc.query("fails_qc").reset_index(
    drop=True
)
print(f"Here are barcode / serum-replicates that fail {curvefit_qc=}")
display(barcode_serum_replicates_fail_qc)

if len(barcode_serum_replicates_fail_qc):
    print("\nCurves for viruses and serum-replicates with at least one failed barcode:")
    fig, _ = fits_noqc.plotReplicates(
        sera=sorted(barcode_serum_replicates_fail_qc["serum_replicate"].unique()),
        viruses=sorted(barcode_serum_replicates_fail_qc["virus"].unique()),
        attempt_shared_legend=False,
        legendfontsize=8,
        titlesize=10,
        ticksize=10,
        ncol=6,
        draw_in_bounds=True,
    )
    display(fig)
    plt.close(fig)

# drop barcode / serum-replicates failing QC
for qc_filter in ["max_frac_infectivity_at_least", "goodness_of_fit"]:
    fits_qc_drops = list(
        fit_params_noqc.query(f"fails_{qc_filter} and (not ignore_qc)")[
            ["barcode", "serum_replicate"]
        ].itertuples(index=False, name=None)
    )
    print(
        f"\nDropping {len(fits_qc_drops)} barcode/serum-replicates for failing "
        f"{qc_filter}={curvefit_qc[qc_filter]}: " + str(fits_qc_drops)
    )
    qc_drops["barcode_serum_replicates"].update({w: qc_filter for w in fits_qc_drops})
    frac_infectivity = frac_infectivity[
        ~frac_infectivity.assign(
            barcode_serum_replicate=lambda x: x.apply(
                lambda r: (r["barcode"], r["serum_replicate"]), axis=1
            )
        )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
    ]
    fit_params_noqc = fit_params_noqc[
        ~fit_params_noqc.assign(
            barcode_serum_replicate=lambda x: x.apply(
                lambda r: (r["barcode"], r["serum_replicate"]), axis=1
            )
        )["barcode_serum_replicate"].isin(qc_drops["barcode_serum_replicates"])
    ]
Here are barcode / serum-replicates that fail curvefit_qc={'max_frac_infectivity_at_least': 0.0, 'goodness_of_fit': {'min_R2': 0.5, 'max_RMSD': 0.15}, 'serum_replicates_ignore_curvefit_qc': [], 'barcode_serum_replicates_ignore_curvefit_qc': []}
serum_replicate barcode max_frac_infectivity virus r2 rmsd fails_max_frac_infectivity_at_least fails_goodness_of_fit fails_qc ignore_qc
0 A230212d0_rd256 AAACTTCGTGGTATAC 0.857328 A/Solwezi/13-NIC-001/2023 0.047393 0.309587 False True True False
1 A230212d0_rd256 AAATTTTTCTTTAGAC 1.000000 A/Jeju/1047/2023 0.235728 0.379251 False True True False
2 A230212d0_rd256 AACAAGGCCAACATTT 1.000000 A/Netherlands/01760/2023 0.282557 0.314315 False True True False
3 A230212d0_rd256 AACAATTAATTTTTCA 1.000000 A/Bhutan/0845/2023 0.257809 0.365692 False True True False
4 A230212d0_rd256 AACCACCCCAGAGATG 1.000000 A/Kansas/14/2017 0.185659 0.315785 False True True False
... ... ... ... ... ... ... ... ... ... ...
161 A230212d28_rd256 TTAGCAGTTAACGTAT 0.964308 A/YAMAGATA/98/2023 0.281747 0.256854 False True True False
162 A230212d28_rd256 TTAGTCATCTGGGTGC 1.000000 A/SouthSudan/631/2023 0.361687 0.324690 False True True False
163 A230212d28_rd256 TTATAATGGCCGGTAT 0.793195 A/EHIME/50/2023 0.449549 0.209317 False True True False
164 A230212d28_rd256 TTTACTCGACTAACCG 1.000000 A/Romania/543634/2022 -0.037478 0.286885 False True True False
165 A230212d28_rd256 TTTATGCCGATAGAGA 0.828160 A/South_Africa/R05384/2023 0.454064 0.204560 False True True False

166 rows × 10 columns

Curves for viruses and serum-replicates with at least one failed barcode:
No description has been provided for this image
Dropping 0 barcode/serum-replicates for failing max_frac_infectivity_at_least=0.0: []

Dropping 166 barcode/serum-replicates for failing goodness_of_fit={'min_R2': 0.5, 'max_RMSD': 0.15}: [('AAACTTCGTGGTATAC', 'A230212d0_rd256'), ('AAATTTTTCTTTAGAC', 'A230212d0_rd256'), ('AACAAGGCCAACATTT', 'A230212d0_rd256'), ('AACAATTAATTTTTCA', 'A230212d0_rd256'), ('AACCACCCCAGAGATG', 'A230212d0_rd256'), ('AACGACAACCATGAAT', 'A230212d0_rd256'), ('AAGCGGTTTAGGTCCA', 'A230212d0_rd256'), ('AAGCTAATCGTAGTCC', 'A230212d0_rd256'), ('AAGTGGTAGGATAAAA', 'A230212d0_rd256'), ('AAGTTGTACTTAAGGC', 'A230212d0_rd256'), ('AATGACAGCTGTCTAG', 'A230212d0_rd256'), ('AATTAATGGTAATAAA', 'A230212d0_rd256'), ('AATTACGCATAGGCCA', 'A230212d0_rd256'), ('ACCGTTTTTCTACCAG', 'A230212d0_rd256'), ('ACGTAAATCCCCACAA', 'A230212d0_rd256'), ('ACTATACATAGAAGAA', 'A230212d0_rd256'), ('AGAAAAACGACATCAT', 'A230212d0_rd256'), ('AGATGGAGGAATAAAC', 'A230212d0_rd256'), ('AGCAGACACTTTACAT', 'A230212d0_rd256'), ('AGCTATGCCTAGTGAA', 'A230212d0_rd256'), ('AGGGACTTTATTGTCC', 'A230212d0_rd256'), ('ATAAAGAATCCCTTGA', 'A230212d0_rd256'), ('ATCGATTCGATTGACG', 'A230212d0_rd256'), ('ATTTATATTGTCGAAC', 'A230212d0_rd256'), ('CAACGTGATGAGGAAG', 'A230212d0_rd256'), ('CAAGAAATGTAGTGAA', 'A230212d0_rd256'), ('CAATGGATAATGATAG', 'A230212d0_rd256'), ('CAATTCGCCGTTCCCC', 'A230212d0_rd256'), ('CACCTTCATCCTAAAG', 'A230212d0_rd256'), ('CACGTTAGTGAGACTT', 'A230212d0_rd256'), ('CAGTAGCAAAACATGC', 'A230212d0_rd256'), ('CATAATGCACAAACGC', 'A230212d0_rd256'), ('CATATTCTAAAATTGA', 'A230212d0_rd256'), ('CCAATCATGTATCACA', 'A230212d0_rd256'), ('CCACAAGTTTGAAAAC', 'A230212d0_rd256'), ('CCACGCACTTAAATAA', 'A230212d0_rd256'), ('CCCATATACTCACAGA', 'A230212d0_rd256'), ('CCCGCTAACCCTGTCT', 'A230212d0_rd256'), ('CCGATAAGACGTCGCT', 'A230212d0_rd256'), ('CCGCAATGACAATTTG', 'A230212d0_rd256'), ('CCGCTATCATTAACCC', 'A230212d0_rd256'), ('CGCGAACAACAGGGGA', 'A230212d0_rd256'), ('CTAAGGGCCTGTTCTT', 'A230212d0_rd256'), ('CTGCGAATATTGTGAC', 'A230212d0_rd256'), ('CTGTGAAAAAAAATAC', 'A230212d0_rd256'), ('CTTACAAAGGTAATTC', 'A230212d0_rd256'), ('CTTGAATACACAAACA', 'A230212d0_rd256'), ('GAAAATCGAGCTTTAA', 'A230212d0_rd256'), ('GAAAGAAAGCTATATG', 'A230212d0_rd256'), ('GACCAAAGCTGCAGGG', 'A230212d0_rd256'), ('GAGATAGCCCAGAGGT', 'A230212d0_rd256'), ('GAGCCCGAATAGCAAG', 'A230212d0_rd256'), ('GCCGTAGCGAAATCTT', 'A230212d0_rd256'), ('GCTGGTGCACAAGATT', 'A230212d0_rd256'), ('GCTTTTGAGAACCATT', 'A230212d0_rd256'), ('GGCTATATATCTGTTT', 'A230212d0_rd256'), ('GGTTGCGTAGTTAATC', 'A230212d0_rd256'), ('GTAGAAACTAGGAGTT', 'A230212d0_rd256'), ('GTTATAGAGTGCTTAC', 'A230212d0_rd256'), ('GTTATTATGACTTCAT', 'A230212d0_rd256'), ('TAAACAAAACCTATAC', 'A230212d0_rd256'), ('TAATAACTTGAGATTC', 'A230212d0_rd256'), ('TACGACGGAAACAGAA', 'A230212d0_rd256'), ('TAGCTGGGCAAAGGCT', 'A230212d0_rd256'), ('TATCAATTCGGTATTA', 'A230212d0_rd256'), ('TCAATGAATGCGGGGT', 'A230212d0_rd256'), ('TCCAAACAGCGTTAAA', 'A230212d0_rd256'), ('TCCCGAACTGAACGCG', 'A230212d0_rd256'), ('TCCGCCACTATAACAT', 'A230212d0_rd256'), ('TCCTTTAACTAATCGA', 'A230212d0_rd256'), ('TCGTCCTAGAACCTAA', 'A230212d0_rd256'), ('TCTTGAATTTCATGGA', 'A230212d0_rd256'), ('TGACAAACACCTGAGG', 'A230212d0_rd256'), ('TGATTCGTCAATTCAT', 'A230212d0_rd256'), ('TGGCTAGCGCACACCA', 'A230212d0_rd256'), ('TTAACCTAACGTATAG', 'A230212d0_rd256'), ('TTATGTTTTAATGGTA', 'A230212d0_rd256'), ('TTGACTCACCGAATAA', 'A230212d0_rd256'), ('TTTATGCCGATAGAGA', 'A230212d0_rd256'), ('TTTCATATAATTTGAG', 'A230212d0_rd256'), ('AAAACAGGTCCGGTTT', 'A230212d28_rd256'), ('AAACTTCGTGGTATAC', 'A230212d28_rd256'), ('AAAGATAAATTCAAAA', 'A230212d28_rd256'), ('AAATACCCTTGAGATA', 'A230212d28_rd256'), ('AACAAGGCCAACATTT', 'A230212d28_rd256'), ('AACAATTAATTTTTCA', 'A230212d28_rd256'), ('AACCACCCCAGAGATG', 'A230212d28_rd256'), ('AAGAAAACGGAAAGAA', 'A230212d28_rd256'), ('AAGCGGTTTAGGTCCA', 'A230212d28_rd256'), ('AAGCTAATCGTAGTCC', 'A230212d28_rd256'), ('AAGTTGTACTTAAGGC', 'A230212d28_rd256'), ('AATCTTTCCAATCTTG', 'A230212d28_rd256'), ('AATGACAGCTGTCTAG', 'A230212d28_rd256'), ('ACAGTACAGATATGAC', 'A230212d28_rd256'), ('ACCGTTTTTCTACCAG', 'A230212d28_rd256'), ('ACGTAAATCCCCACAA', 'A230212d28_rd256'), ('AGATCATAAGCAATAA', 'A230212d28_rd256'), ('AGATGGAGGAATAAAC', 'A230212d28_rd256'), ('AGCATGAGCTTGTCAT', 'A230212d28_rd256'), ('AGCTATGCCTAGTGAA', 'A230212d28_rd256'), ('AGGGACTTTATTGTCC', 'A230212d28_rd256'), ('AGTTATGTAAAACGTG', 'A230212d28_rd256'), ('ATAAAGAATCCCTTGA', 'A230212d28_rd256'), ('ATCGAAAAAACTGCAA', 'A230212d28_rd256'), ('ATTTATATTGTCGAAC', 'A230212d28_rd256'), ('CAAGACAAGCCCTATA', 'A230212d28_rd256'), ('CACCTTCATCCTAAAG', 'A230212d28_rd256'), ('CACGTTAGTGAGACTT', 'A230212d28_rd256'), ('CAGTAGCAAAACATGC', 'A230212d28_rd256'), ('CAGTGCCATCCATCCA', 'A230212d28_rd256'), ('CCAATAAAATACGATG', 'A230212d28_rd256'), ('CCAATCATGTATCACA', 'A230212d28_rd256'), ('CCCATATACTCACAGA', 'A230212d28_rd256'), ('CCGCTATCATTAACCC', 'A230212d28_rd256'), ('CCGGTTTTCGGGACCT', 'A230212d28_rd256'), ('CGATCTTTACGAAAAA', 'A230212d28_rd256'), ('CGCCGAACGGCGGCGC', 'A230212d28_rd256'), ('CGCGAACAACAGGGGA', 'A230212d28_rd256'), ('CGCGAATCACTAAGTA', 'A230212d28_rd256'), ('CGTATAACTGACGATT', 'A230212d28_rd256'), ('CTACACTACATCAAAT', 'A230212d28_rd256'), ('CTACTAGAGCAGCGAG', 'A230212d28_rd256'), ('CTATCTTAATCTACAG', 'A230212d28_rd256'), ('CTCATTACAGAAATTG', 'A230212d28_rd256'), ('CTGTGAAAAAAAATAC', 'A230212d28_rd256'), ('CTTCATCTCATTTAAA', 'A230212d28_rd256'), ('CTTGAATACACAAACA', 'A230212d28_rd256'), ('GAAAGAAAGCTATATG', 'A230212d28_rd256'), ('GACAGAAACAAAATTA', 'A230212d28_rd256'), ('GACCAAAGCTGCAGGG', 'A230212d28_rd256'), ('GAGATAGCCCAGAGGT', 'A230212d28_rd256'), ('GCAACGAGGTGTAACC', 'A230212d28_rd256'), ('GCATTATAATCTTGTG', 'A230212d28_rd256'), ('GCCGTAGCGAAATCTT', 'A230212d28_rd256'), ('GCTAATTCCAAAAGCG', 'A230212d28_rd256'), ('GCTGAAGACAGTATTA', 'A230212d28_rd256'), ('GCTTTTGAGAACCATT', 'A230212d28_rd256'), ('GGGTGCAATGAATCCA', 'A230212d28_rd256'), ('GGTTGCGTAGTTAATC', 'A230212d28_rd256'), ('GTAGAAACTAGGAGTT', 'A230212d28_rd256'), ('GTTATTATGACTTCAT', 'A230212d28_rd256'), ('GTTTTTCACTGAGTAG', 'A230212d28_rd256'), ('TACCATTTTGGTCCGC', 'A230212d28_rd256'), ('TACGAAAATCAAGAGC', 'A230212d28_rd256'), ('TACGACGGAAACAGAA', 'A230212d28_rd256'), ('TAGCTGGGCAAAGGCT', 'A230212d28_rd256'), ('TAGTTGCCCCGACCTG', 'A230212d28_rd256'), ('TATCGCAATATGATAA', 'A230212d28_rd256'), ('TATTATCTAAACGGCG', 'A230212d28_rd256'), ('TCAAACTATGATATTC', 'A230212d28_rd256'), ('TCAACCCTTCGATGTA', 'A230212d28_rd256'), ('TCAATGAATGCGGGGT', 'A230212d28_rd256'), ('TCCAAACAGCGTTAAA', 'A230212d28_rd256'), ('TCCCGAACTGAACGCG', 'A230212d28_rd256'), ('TCCTTTAACTAATCGA', 'A230212d28_rd256'), ('TCGCGGTAGATTTGCG', 'A230212d28_rd256'), ('TCGTCCTAGAACCTAA', 'A230212d28_rd256'), ('TCTTAACTACCCGATG', 'A230212d28_rd256'), ('TGAGATCAGCCGGGTG', 'A230212d28_rd256'), ('TGGCTAGCGCACACCA', 'A230212d28_rd256'), ('TTACGTCAATGTTTGA', 'A230212d28_rd256'), ('TTAGCAGTTAACGTAT', 'A230212d28_rd256'), ('TTAGTCATCTGGGTGC', 'A230212d28_rd256'), ('TTATAATGGCCGGTAT', 'A230212d28_rd256'), ('TTTACTCGACTAACCG', 'A230212d28_rd256'), ('TTTATGCCGATAGAGA', 'A230212d28_rd256')]

Fit neutralization curves after applying QC¶

No we re-fit curves after applying all the QC:

In [20]:
fits_qc = neutcurve.CurveFits(
    frac_infectivity.rename(
        columns={
            "frac_infectivity_ceiling": "fraction infectivity",
            "concentration": "serum concentration",
        }
    ),
    conc_col="serum concentration",
    fracinf_col="fraction infectivity",
    virus_col="strain",
    serum_col="serum",
    replicate_col="plate_barcode",
    fixtop=curvefit_params["fixtop"],
    fixbottom=curvefit_params["fixbottom"],
    fixslope=curvefit_params["fixslope"],
)

fit_params_qc = fits_qc.fitParams(average_only=False, no_average=True)
assert len(fit_params_qc) <= len(
    fits_noqc.fitParams(average_only=False, no_average=True)
)

print(f"Assigning fits for this plate to {group}")
fit_params_qc.insert(0, "group", group)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
/fh/fast/bloom_j/computational_notebooks/ckikawa/2024/flu_seqneut_H3N2_2023-2024/.snakemake/conda/3d7bcea0b75814e39f27531956478cb3_/lib/python3.12/site-packages/neutcurve/hillcurve.py:1177: RuntimeWarning: invalid value encountered in power
  return b + (t - b) / (1 + (c / m) ** s)
Assigning fits for this plate to pilot

Plot all the curves that passed QC:

In [21]:
if fits_qc.sera:
    _ = fits_qc.plotReplicates(
        attempt_shared_legend=False,
        legendfontsize=8,
        titlesize=10,
        ticksize=10,
        ncol=6,
        draw_in_bounds=True,
    )
else:
    print("No sera passed QC.")
No description has been provided for this image

Save results to files¶

In [22]:
print(f"Writing fraction infectivities to {frac_infectivity_csv}")
(
    frac_infectivity[
        [
            "serum",
            "strain",
            "plate_barcode",
            "dilution_factor",
            "frac_infectivity_raw",
            "frac_infectivity_ceiling",
        ]
    ]
    .sort_values(["serum", "plate_barcode", "dilution_factor"])
    .to_csv(frac_infectivity_csv, index=False, float_format="%.4g")
)

print(f"\nWriting fit parameters to {fits_csv}")
(
    fit_params_qc.drop(columns=["nreplicates", "ic50_str"]).to_csv(
        fits_csv, index=False, float_format="%.4g"
    )
)

print(f"\nPickling neutcurve.CurveFits object for these data to {fits_pickle}")
with open(fits_pickle, "wb") as f:
    pickle.dump(fits_qc, f)

print(f"\nWriting QC drops to {qc_drops_yaml}")


def tup_to_str(x):
    return " ".join(x) if isinstance(x, tuple) else x


qc_drops_for_yaml = {
    key: {tup_to_str(key2): val2 for key2, val2 in val.items()}
    for key, val in qc_drops.items()
}
with open(qc_drops_yaml, "w") as f:
    yaml.YAML(typ="rt").dump(qc_drops_for_yaml, f)
print("\nHere are the QC drops:\n***************************")
yaml.YAML(typ="rt").dump(qc_drops_for_yaml, sys.stdout)
Writing fraction infectivities to results/plates/plate6/frac_infectivity.csv

Writing fit parameters to results/plates/plate6/curvefits.csv

Pickling neutcurve.CurveFits object for these data to results/plates/plate6/curvefits.pickle

Writing QC drops to results/plates/plate6/qc_drops.yml

Here are the QC drops:
***************************
wells: {}
barcodes:
  AAAGTAGCAGAGGATT: min_neut_standard_frac_per_well
  AAATTCACAATATCCA: min_neut_standard_frac_per_well
  AGACCATCGCACCCAA: min_neut_standard_frac_per_well
  ATAACGTTTGTGCAAA: min_neut_standard_frac_per_well
  CAAAAGCAGCACGATA: min_neut_standard_frac_per_well
  CACCGACCAACTCTCT: min_neut_standard_frac_per_well
  CATAAAAGACTGTATA: min_neut_standard_frac_per_well
  CCAGAGACACGCTAGG: min_neut_standard_frac_per_well
  CCCTCCTCAAGGGTAA: min_neut_standard_frac_per_well
  CCTATAAGGCCTTACG: min_neut_standard_frac_per_well
  CGTACGTATGTCCCAG: min_neut_standard_frac_per_well
  CGTCCCTGGCGTGTCG: min_neut_standard_frac_per_well
  CGTTAACGGCCTATCC: min_neut_standard_frac_per_well
  CTCCAATAGGAGACGA: min_neut_standard_frac_per_well
  TATATGGAATACTAAA: min_neut_standard_frac_per_well
  TCTCCGATAGCCCTAC: min_neut_standard_frac_per_well
  TGTTGTAATCTGAATA: min_neut_standard_frac_per_well
barcode_wells:
  AAATGCTGAGAGGGTA C12: min_no_serum_count_per_viral_barcode_well
  AGCAGACACTTTACAT C12: min_no_serum_count_per_viral_barcode_well
  CAATTCGCCGTTCCCC C12: min_no_serum_count_per_viral_barcode_well
  GTAGAAACTAGGAGTT C12: min_no_serum_count_per_viral_barcode_well
  GTAATTCGCATGCGGA C12: min_no_serum_count_per_viral_barcode_well
  CAAGACAAGCCCTATA C12: min_no_serum_count_per_viral_barcode_well
  TCCCCGTGGTTTGACA C12: min_no_serum_count_per_viral_barcode_well
  CCAATCCCAGCCTTTA C12: min_no_serum_count_per_viral_barcode_well
  TTACATTTTTAGAATT C12: min_no_serum_count_per_viral_barcode_well
  CTTCATCTCATTTAAA G12: min_no_serum_count_per_viral_barcode_well
  TCCGCCACTATAACAT G12: min_no_serum_count_per_viral_barcode_well
  CCGCTATCATTAACCC G12: min_no_serum_count_per_viral_barcode_well
  ACAAAGATAAAAATTT G12: min_no_serum_count_per_viral_barcode_well
  CACGTTAGTGAGACTT G12: min_no_serum_count_per_viral_barcode_well
  ATGCGTCTAAACATAG G12: min_no_serum_count_per_viral_barcode_well
  ATCGAAAAAACTGCAA C2: max_frac_infectivity_per_viral_barcode_well
  TCCCGAACTGAACGCG C2: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA C3: max_frac_infectivity_per_viral_barcode_well
  TTAACCTAACGTATAG C3: max_frac_infectivity_per_viral_barcode_well
  TCGCGGTAGATTTGCG C3: max_frac_infectivity_per_viral_barcode_well
  ACAAAGATAAAAATTT C3: max_frac_infectivity_per_viral_barcode_well
  AAACTTCGTGGTATAC C4: max_frac_infectivity_per_viral_barcode_well
  AAGAAATTATGGCAGG C4: max_frac_infectivity_per_viral_barcode_well
  CGCAAGGGATACTAAC C5: max_frac_infectivity_per_viral_barcode_well
  TGGCTAGCGCACACCA C5: max_frac_infectivity_per_viral_barcode_well
  GCTGAAGACAGTATTA C5: max_frac_infectivity_per_viral_barcode_well
  AAAGATAAATTCAAAA C5: max_frac_infectivity_per_viral_barcode_well
  ATCGATTCGATTGACG C6: max_frac_infectivity_per_viral_barcode_well
  CGTCAGAAGTTTATAA C6: max_frac_infectivity_per_viral_barcode_well
  CCGGTTTTCGGGACCT C6: max_frac_infectivity_per_viral_barcode_well
  CAAGACAAGCCCTATA C6: max_frac_infectivity_per_viral_barcode_well
  CTCATTACAGAAATTG C6: max_frac_infectivity_per_viral_barcode_well
  AATCCGGTTAACCCCG C6: max_frac_infectivity_per_viral_barcode_well
  TGTAGTATAAGAATAA C6: max_frac_infectivity_per_viral_barcode_well
  GATTCACGGCCCACAA C6: max_frac_infectivity_per_viral_barcode_well
  CATAATGCACAAACGC C6: max_frac_infectivity_per_viral_barcode_well
  ACAAAGATAAAAATTT C6: max_frac_infectivity_per_viral_barcode_well
  ATCGAAAAAACTGCAA C6: max_frac_infectivity_per_viral_barcode_well
  GAAGTAACAAACTATG C7: max_frac_infectivity_per_viral_barcode_well
  AAATTTTTCTTTAGAC C7: max_frac_infectivity_per_viral_barcode_well
  CGCGAATCACTAAGTA C7: max_frac_infectivity_per_viral_barcode_well
  CGCAGTACACAACAAG C7: max_frac_infectivity_per_viral_barcode_well
  ACTGAACAGTATAACT C7: max_frac_infectivity_per_viral_barcode_well
  AATCCGATAAGAGCTA C7: max_frac_infectivity_per_viral_barcode_well
  TCAAACTATGATATTC C7: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA C7: max_frac_infectivity_per_viral_barcode_well
  CAAGACAAGCCCTATA C7: max_frac_infectivity_per_viral_barcode_well
  CACCACAGTTACTTAA C7: max_frac_infectivity_per_viral_barcode_well
  AATCCGGTTAACCCCG C7: max_frac_infectivity_per_viral_barcode_well
  CCGATAAGACGTCGCT C8: max_frac_infectivity_per_viral_barcode_well
  CTGCGAATATTGTGAC C8: max_frac_infectivity_per_viral_barcode_well
  ATTAGATTATAACGTA C8: max_frac_infectivity_per_viral_barcode_well
  AGCTGAATTAAGTATG C8: max_frac_infectivity_per_viral_barcode_well
  TAGTTGCCCCGACCTG C8: max_frac_infectivity_per_viral_barcode_well
  TGTAGTATAAGAATAA C8: max_frac_infectivity_per_viral_barcode_well
  TACGAAAATCAAGAGC C8: max_frac_infectivity_per_viral_barcode_well
  ATCGAAAAAACTGCAA C8: max_frac_infectivity_per_viral_barcode_well
  ATCGTCCCGGACATTT C8: max_frac_infectivity_per_viral_barcode_well
  TCCACACCCCTAGCTA C8: max_frac_infectivity_per_viral_barcode_well
  TTTCATATAATTTGAG C8: max_frac_infectivity_per_viral_barcode_well
  AATCCGGTTAACCCCG C8: max_frac_infectivity_per_viral_barcode_well
  ACAAAGATAAAAATTT C8: max_frac_infectivity_per_viral_barcode_well
  TTGCAATTGAAACATA C9: max_frac_infectivity_per_viral_barcode_well
  AAACTTCGTGGTATAC C9: max_frac_infectivity_per_viral_barcode_well
  TAGTTGCCCCGACCTG C9: max_frac_infectivity_per_viral_barcode_well
  AAGTTGTACTTAAGGC C9: max_frac_infectivity_per_viral_barcode_well
  AATATACCGGCACTAC C9: max_frac_infectivity_per_viral_barcode_well
  CACCCAAACGTTCGCA C9: max_frac_infectivity_per_viral_barcode_well
  TGACAAACACCTGAGG C9: max_frac_infectivity_per_viral_barcode_well
  TCTTAACTACCCGATG C9: max_frac_infectivity_per_viral_barcode_well
  TGAATTGCGTGATGGG C9: max_frac_infectivity_per_viral_barcode_well
  AATCTTTCCAATCTTG C9: max_frac_infectivity_per_viral_barcode_well
  GGGTGCAATGAATCCA C9: max_frac_infectivity_per_viral_barcode_well
  TGTAGTATAAGAATAA C9: max_frac_infectivity_per_viral_barcode_well
  TGCCGATCCAATTGAT C9: max_frac_infectivity_per_viral_barcode_well
  TTTACTCGACTAACCG C9: max_frac_infectivity_per_viral_barcode_well
  TTTCATATAATTTGAG C9: max_frac_infectivity_per_viral_barcode_well
  TCCCGAACTGAACGCG C9: max_frac_infectivity_per_viral_barcode_well
  CCGCTATCATTAACCC C9: max_frac_infectivity_per_viral_barcode_well
  ACAAAGATAAAAATTT C9: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA C10: max_frac_infectivity_per_viral_barcode_well
  CGTCAGAAGTTTATAA C10: max_frac_infectivity_per_viral_barcode_well
  AGACTGTACGCGACAG C10: max_frac_infectivity_per_viral_barcode_well
  AAGTGGTAGGATAAAA C10: max_frac_infectivity_per_viral_barcode_well
  TTATGTTTTAATGGTA C10: max_frac_infectivity_per_viral_barcode_well
  CGCGAATCACTAAGTA C10: max_frac_infectivity_per_viral_barcode_well
  ATCGAAAAAACTGCAA C10: max_frac_infectivity_per_viral_barcode_well
  ATAAAGAATCCCTTGA C10: max_frac_infectivity_per_viral_barcode_well
  ATCGTCCCGGACATTT C10: max_frac_infectivity_per_viral_barcode_well
  CCGCTATCATTAACCC C10: max_frac_infectivity_per_viral_barcode_well
  CACCACAGTTACTTAA C10: max_frac_infectivity_per_viral_barcode_well
  AATCCGGTTAACCCCG C10: max_frac_infectivity_per_viral_barcode_well
  ATTAGATTATAACGTA C11: max_frac_infectivity_per_viral_barcode_well
  TCAATGAATGCGGGGT C11: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA C11: max_frac_infectivity_per_viral_barcode_well
  AAAGATAAATTCAAAA C11: max_frac_infectivity_per_viral_barcode_well
  CGCGAACAACAGGGGA C11: max_frac_infectivity_per_viral_barcode_well
  CCGGTTTTCGGGACCT C11: max_frac_infectivity_per_viral_barcode_well
  TATACTCACGGAGGAT C11: max_frac_infectivity_per_viral_barcode_well
  AAATGCTGAGAGGGTA C11: max_frac_infectivity_per_viral_barcode_well
  AGTGCTATAAAAATCA C11: max_frac_infectivity_per_viral_barcode_well
  ATAAAGAATCCCTTGA C11: max_frac_infectivity_per_viral_barcode_well
  CGGCTAAAGTCTATAG C11: max_frac_infectivity_per_viral_barcode_well
  AACGACAACCATGAAT C11: max_frac_infectivity_per_viral_barcode_well
  AATTACGCATAGGCCA C11: max_frac_infectivity_per_viral_barcode_well
  CGCAGTACACAACAAG C11: max_frac_infectivity_per_viral_barcode_well
  TTTCATATAATTTGAG C11: max_frac_infectivity_per_viral_barcode_well
  ATCGAAAAAACTGCAA C11: max_frac_infectivity_per_viral_barcode_well
  AGAAAAACGACATCAT C11: max_frac_infectivity_per_viral_barcode_well
  AATCCGGTTAACCCCG C11: max_frac_infectivity_per_viral_barcode_well
  GATTCACGGCCCACAA C11: max_frac_infectivity_per_viral_barcode_well
  CACCACAGTTACTTAA C11: max_frac_infectivity_per_viral_barcode_well
  CGCCGAACGGCGGCGC C11: max_frac_infectivity_per_viral_barcode_well
  GTAGAAACTAGGAGTT C11: max_frac_infectivity_per_viral_barcode_well
  CCATCACCTTATACAC G2: max_frac_infectivity_per_viral_barcode_well
  AAGTTGTACTTAAGGC G3: max_frac_infectivity_per_viral_barcode_well
  ACAGTACAGATATGAC G4: max_frac_infectivity_per_viral_barcode_well
  AATCTTTCCAATCTTG G4: max_frac_infectivity_per_viral_barcode_well
  AAGTTGTACTTAAGGC G4: max_frac_infectivity_per_viral_barcode_well
  ACAAAGATAAAAATTT G4: max_frac_infectivity_per_viral_barcode_well
  TATCATTTCATCTACA G5: max_frac_infectivity_per_viral_barcode_well
  AATATACCGGCACTAC G5: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA G5: max_frac_infectivity_per_viral_barcode_well
  CGTCAGAAGTTTATAA G5: max_frac_infectivity_per_viral_barcode_well
  CTCATTACAGAAATTG G5: max_frac_infectivity_per_viral_barcode_well
  AATTACGCATAGGCCA G5: max_frac_infectivity_per_viral_barcode_well
  CACCACAGTTACTTAA G5: max_frac_infectivity_per_viral_barcode_well
  CAAGACAAGCCCTATA G5: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA G6: max_frac_infectivity_per_viral_barcode_well
  CGTATAACTGACGATT G6: max_frac_infectivity_per_viral_barcode_well
  AAGAAATTATGGCAGG G6: max_frac_infectivity_per_viral_barcode_well
  TGTAGTATAAGAATAA G7: max_frac_infectivity_per_viral_barcode_well
  CTGCGAATATTGTGAC G7: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA G7: max_frac_infectivity_per_viral_barcode_well
  AATCTTTCCAATCTTG G7: max_frac_infectivity_per_viral_barcode_well
  CCGGTTTTCGGGACCT G7: max_frac_infectivity_per_viral_barcode_well
  TTTCATATAATTTGAG G7: max_frac_infectivity_per_viral_barcode_well
  ATCGAAAAAACTGCAA G7: max_frac_infectivity_per_viral_barcode_well
  CAAGACAAGCCCTATA G7: max_frac_infectivity_per_viral_barcode_well
  ACAAAGATAAAAATTT G7: max_frac_infectivity_per_viral_barcode_well
  TACGAAAATCAAGAGC G8: max_frac_infectivity_per_viral_barcode_well
  GCATTATAATCTTGTG G8: max_frac_infectivity_per_viral_barcode_well
  TTGACTCACCGAATAA G8: max_frac_infectivity_per_viral_barcode_well
  ATAATAATCATCAAGA G8: max_frac_infectivity_per_viral_barcode_well
  ACCGTTTTTCTACCAG G8: max_frac_infectivity_per_viral_barcode_well
  CGTCAGAAGTTTATAA G8: max_frac_infectivity_per_viral_barcode_well
  AGTGCTATAAAAATCA G8: max_frac_infectivity_per_viral_barcode_well
  AGCTATGCCTAGTGAA G8: max_frac_infectivity_per_viral_barcode_well
  ACCCTTTTAGATATGA G8: max_frac_infectivity_per_viral_barcode_well
  AATTACGCATAGGCCA G8: max_frac_infectivity_per_viral_barcode_well
  AATCCGGTTAACCCCG G8: max_frac_infectivity_per_viral_barcode_well
  CAATTCGCCGTTCCCC G8: max_frac_infectivity_per_viral_barcode_well
  CCGCTATCATTAACCC G8: max_frac_infectivity_per_viral_barcode_well
  CACCACAGTTACTTAA G8: max_frac_infectivity_per_viral_barcode_well
  ATGCGTCTAAACATAG G8: max_frac_infectivity_per_viral_barcode_well
  AATGACAGCTGTCTAG G8: max_frac_infectivity_per_viral_barcode_well
  TGATTCGTCAATTCAT G9: max_frac_infectivity_per_viral_barcode_well
  GACCAAAAAGCAGTAT G9: max_frac_infectivity_per_viral_barcode_well
  AGACTGTACGCGACAG G9: max_frac_infectivity_per_viral_barcode_well
  GACCAAAGCTGCAGGG G9: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA G9: max_frac_infectivity_per_viral_barcode_well
  GGGTGCAATGAATCCA G9: max_frac_infectivity_per_viral_barcode_well
  CGCCGAACGGCGGCGC G9: max_frac_infectivity_per_viral_barcode_well
  CAAGACAAGCCCTATA G9: max_frac_infectivity_per_viral_barcode_well
  TCCCGAACTGAACGCG G9: max_frac_infectivity_per_viral_barcode_well
  TCTTACATTGAAAGGC G10: max_frac_infectivity_per_viral_barcode_well
  GCCCATTGAACGCAGC G10: max_frac_infectivity_per_viral_barcode_well
  CTGTGAAAAAAAATAC G10: max_frac_infectivity_per_viral_barcode_well
  GCTGGTGCACAAGATT G10: max_frac_infectivity_per_viral_barcode_well
  GGTTGCGTAGTTAATC G10: max_frac_infectivity_per_viral_barcode_well
  CGATCTTTACGAAAAA G10: max_frac_infectivity_per_viral_barcode_well
  AACAAGGCCAACATTT G10: max_frac_infectivity_per_viral_barcode_well
  TTAGCTACTAACCCGT G10: max_frac_infectivity_per_viral_barcode_well
  AGCAGACACTTTACAT G10: max_frac_infectivity_per_viral_barcode_well
  ACCCTTTTAGATATGA G10: max_frac_infectivity_per_viral_barcode_well
  AATCCGATAAGAGCTA G10: max_frac_infectivity_per_viral_barcode_well
  GAAGTAACAAACTATG G10: max_frac_infectivity_per_viral_barcode_well
  TAATAACTTGAGATTC G10: max_frac_infectivity_per_viral_barcode_well
  AAGTGGTAGGATAAAA G10: max_frac_infectivity_per_viral_barcode_well
  TTATAATGGCCGGTAT G10: max_frac_infectivity_per_viral_barcode_well
  CCCGCTAACCCTGTCT G10: max_frac_infectivity_per_viral_barcode_well
  CGCGAACAACAGGGGA G10: max_frac_infectivity_per_viral_barcode_well
  TGCCGATCCAATTGAT G10: max_frac_infectivity_per_viral_barcode_well
  CACCACAGTTACTTAA G10: max_frac_infectivity_per_viral_barcode_well
  CAAGACAAGCCCTATA G10: max_frac_infectivity_per_viral_barcode_well
  CCGCTATCATTAACCC G10: max_frac_infectivity_per_viral_barcode_well
  CGCCGAACGGCGGCGC G10: max_frac_infectivity_per_viral_barcode_well
  ATAAAGAATCCCTTGA G11: max_frac_infectivity_per_viral_barcode_well
  AATTACGCATAGGCCA G11: max_frac_infectivity_per_viral_barcode_well
  AACGACAACCATGAAT G11: max_frac_infectivity_per_viral_barcode_well
  AATAGGCCCAAATCCA G11: max_frac_infectivity_per_viral_barcode_well
  CCAATAAAATACGATG G11: max_frac_infectivity_per_viral_barcode_well
  TCAATGAATGCGGGGT G11: max_frac_infectivity_per_viral_barcode_well
  AACGACACTTACATCC G11: max_frac_infectivity_per_viral_barcode_well
  ATTAGATTATAACGTA G11: max_frac_infectivity_per_viral_barcode_well
  CGTCAGAAGTTTATAA G11: max_frac_infectivity_per_viral_barcode_well
  GACCTCCTGGGCACGC G11: max_frac_infectivity_per_viral_barcode_well
  AACAATTAATTTTTCA G11: max_frac_infectivity_per_viral_barcode_well
  CGATCTTTACGAAAAA G11: max_frac_infectivity_per_viral_barcode_well
  AATCCGATAAGAGCTA G11: max_frac_infectivity_per_viral_barcode_well
  CTGAAACCTTGTCCTA G11: max_frac_infectivity_per_viral_barcode_well
  CATAATGCACAAACGC G11: max_frac_infectivity_per_viral_barcode_well
  TCGCGGTAGATTTGCG G11: max_frac_infectivity_per_viral_barcode_well
  AACCACCCCAGAGATG G11: max_frac_infectivity_per_viral_barcode_well
  ACGGACAACCTATCGC G11: max_frac_infectivity_per_viral_barcode_well
  CCGCTATCATTAACCC G11: max_frac_infectivity_per_viral_barcode_well
  TCCACACCCCTAGCTA G11: max_frac_infectivity_per_viral_barcode_well
  ATCGAAAAAACTGCAA G11: max_frac_infectivity_per_viral_barcode_well
  TTACATTTTTAGAATT G11: max_frac_infectivity_per_viral_barcode_well
  CACCACAGTTACTTAA G11: max_frac_infectivity_per_viral_barcode_well
  CGCCGAACGGCGGCGC G11: max_frac_infectivity_per_viral_barcode_well
  ACAAAGATAAAAATTT G11: max_frac_infectivity_per_viral_barcode_well
barcode_serum_replicates:
  AAACTTCGTGGTATAC A230212d0_rd256: goodness_of_fit
  AAATTTTTCTTTAGAC A230212d0_rd256: goodness_of_fit
  AACAAGGCCAACATTT A230212d0_rd256: goodness_of_fit
  AACAATTAATTTTTCA A230212d0_rd256: goodness_of_fit
  AACCACCCCAGAGATG A230212d0_rd256: goodness_of_fit
  AACGACAACCATGAAT A230212d0_rd256: goodness_of_fit
  AAGCGGTTTAGGTCCA A230212d0_rd256: goodness_of_fit
  AAGCTAATCGTAGTCC A230212d0_rd256: goodness_of_fit
  AAGTGGTAGGATAAAA A230212d0_rd256: goodness_of_fit
  AAGTTGTACTTAAGGC A230212d0_rd256: goodness_of_fit
  AATGACAGCTGTCTAG A230212d0_rd256: goodness_of_fit
  AATTAATGGTAATAAA A230212d0_rd256: goodness_of_fit
  AATTACGCATAGGCCA A230212d0_rd256: goodness_of_fit
  ACCGTTTTTCTACCAG A230212d0_rd256: goodness_of_fit
  ACGTAAATCCCCACAA A230212d0_rd256: goodness_of_fit
  ACTATACATAGAAGAA A230212d0_rd256: goodness_of_fit
  AGAAAAACGACATCAT A230212d0_rd256: goodness_of_fit
  AGATGGAGGAATAAAC A230212d0_rd256: goodness_of_fit
  AGCAGACACTTTACAT A230212d0_rd256: goodness_of_fit
  AGCTATGCCTAGTGAA A230212d0_rd256: goodness_of_fit
  AGGGACTTTATTGTCC A230212d0_rd256: goodness_of_fit
  ATAAAGAATCCCTTGA A230212d0_rd256: goodness_of_fit
  ATCGATTCGATTGACG A230212d0_rd256: goodness_of_fit
  ATTTATATTGTCGAAC A230212d0_rd256: goodness_of_fit
  CAACGTGATGAGGAAG A230212d0_rd256: goodness_of_fit
  CAAGAAATGTAGTGAA A230212d0_rd256: goodness_of_fit
  CAATGGATAATGATAG A230212d0_rd256: goodness_of_fit
  CAATTCGCCGTTCCCC A230212d0_rd256: goodness_of_fit
  CACCTTCATCCTAAAG A230212d0_rd256: goodness_of_fit
  CACGTTAGTGAGACTT A230212d0_rd256: goodness_of_fit
  CAGTAGCAAAACATGC A230212d0_rd256: goodness_of_fit
  CATAATGCACAAACGC A230212d0_rd256: goodness_of_fit
  CATATTCTAAAATTGA A230212d0_rd256: goodness_of_fit
  CCAATCATGTATCACA A230212d0_rd256: goodness_of_fit
  CCACAAGTTTGAAAAC A230212d0_rd256: goodness_of_fit
  CCACGCACTTAAATAA A230212d0_rd256: goodness_of_fit
  CCCATATACTCACAGA A230212d0_rd256: goodness_of_fit
  CCCGCTAACCCTGTCT A230212d0_rd256: goodness_of_fit
  CCGATAAGACGTCGCT A230212d0_rd256: goodness_of_fit
  CCGCAATGACAATTTG A230212d0_rd256: goodness_of_fit
  CCGCTATCATTAACCC A230212d0_rd256: goodness_of_fit
  CGCGAACAACAGGGGA A230212d0_rd256: goodness_of_fit
  CTAAGGGCCTGTTCTT A230212d0_rd256: goodness_of_fit
  CTGCGAATATTGTGAC A230212d0_rd256: goodness_of_fit
  CTGTGAAAAAAAATAC A230212d0_rd256: goodness_of_fit
  CTTACAAAGGTAATTC A230212d0_rd256: goodness_of_fit
  CTTGAATACACAAACA A230212d0_rd256: goodness_of_fit
  GAAAATCGAGCTTTAA A230212d0_rd256: goodness_of_fit
  GAAAGAAAGCTATATG A230212d0_rd256: goodness_of_fit
  GACCAAAGCTGCAGGG A230212d0_rd256: goodness_of_fit
  GAGATAGCCCAGAGGT A230212d0_rd256: goodness_of_fit
  GAGCCCGAATAGCAAG A230212d0_rd256: goodness_of_fit
  GCCGTAGCGAAATCTT A230212d0_rd256: goodness_of_fit
  GCTGGTGCACAAGATT A230212d0_rd256: goodness_of_fit
  GCTTTTGAGAACCATT A230212d0_rd256: goodness_of_fit
  GGCTATATATCTGTTT A230212d0_rd256: goodness_of_fit
  GGTTGCGTAGTTAATC A230212d0_rd256: goodness_of_fit
  GTAGAAACTAGGAGTT A230212d0_rd256: goodness_of_fit
  GTTATAGAGTGCTTAC A230212d0_rd256: goodness_of_fit
  GTTATTATGACTTCAT A230212d0_rd256: goodness_of_fit
  TAAACAAAACCTATAC A230212d0_rd256: goodness_of_fit
  TAATAACTTGAGATTC A230212d0_rd256: goodness_of_fit
  TACGACGGAAACAGAA A230212d0_rd256: goodness_of_fit
  TAGCTGGGCAAAGGCT A230212d0_rd256: goodness_of_fit
  TATCAATTCGGTATTA A230212d0_rd256: goodness_of_fit
  TCAATGAATGCGGGGT A230212d0_rd256: goodness_of_fit
  TCCAAACAGCGTTAAA A230212d0_rd256: goodness_of_fit
  TCCCGAACTGAACGCG A230212d0_rd256: goodness_of_fit
  TCCGCCACTATAACAT A230212d0_rd256: goodness_of_fit
  TCCTTTAACTAATCGA A230212d0_rd256: goodness_of_fit
  TCGTCCTAGAACCTAA A230212d0_rd256: goodness_of_fit
  TCTTGAATTTCATGGA A230212d0_rd256: goodness_of_fit
  TGACAAACACCTGAGG A230212d0_rd256: goodness_of_fit
  TGATTCGTCAATTCAT A230212d0_rd256: goodness_of_fit
  TGGCTAGCGCACACCA A230212d0_rd256: goodness_of_fit
  TTAACCTAACGTATAG A230212d0_rd256: goodness_of_fit
  TTATGTTTTAATGGTA A230212d0_rd256: goodness_of_fit
  TTGACTCACCGAATAA A230212d0_rd256: goodness_of_fit
  TTTATGCCGATAGAGA A230212d0_rd256: goodness_of_fit
  TTTCATATAATTTGAG A230212d0_rd256: goodness_of_fit
  AAAACAGGTCCGGTTT A230212d28_rd256: goodness_of_fit
  AAACTTCGTGGTATAC A230212d28_rd256: goodness_of_fit
  AAAGATAAATTCAAAA A230212d28_rd256: goodness_of_fit
  AAATACCCTTGAGATA A230212d28_rd256: goodness_of_fit
  AACAAGGCCAACATTT A230212d28_rd256: goodness_of_fit
  AACAATTAATTTTTCA A230212d28_rd256: goodness_of_fit
  AACCACCCCAGAGATG A230212d28_rd256: goodness_of_fit
  AAGAAAACGGAAAGAA A230212d28_rd256: goodness_of_fit
  AAGCGGTTTAGGTCCA A230212d28_rd256: goodness_of_fit
  AAGCTAATCGTAGTCC A230212d28_rd256: goodness_of_fit
  AAGTTGTACTTAAGGC A230212d28_rd256: goodness_of_fit
  AATCTTTCCAATCTTG A230212d28_rd256: goodness_of_fit
  AATGACAGCTGTCTAG A230212d28_rd256: goodness_of_fit
  ACAGTACAGATATGAC A230212d28_rd256: goodness_of_fit
  ACCGTTTTTCTACCAG A230212d28_rd256: goodness_of_fit
  ACGTAAATCCCCACAA A230212d28_rd256: goodness_of_fit
  AGATCATAAGCAATAA A230212d28_rd256: goodness_of_fit
  AGATGGAGGAATAAAC A230212d28_rd256: goodness_of_fit
  AGCATGAGCTTGTCAT A230212d28_rd256: goodness_of_fit
  AGCTATGCCTAGTGAA A230212d28_rd256: goodness_of_fit
  AGGGACTTTATTGTCC A230212d28_rd256: goodness_of_fit
  AGTTATGTAAAACGTG A230212d28_rd256: goodness_of_fit
  ATAAAGAATCCCTTGA A230212d28_rd256: goodness_of_fit
  ATCGAAAAAACTGCAA A230212d28_rd256: goodness_of_fit
  ATTTATATTGTCGAAC A230212d28_rd256: goodness_of_fit
  CAAGACAAGCCCTATA A230212d28_rd256: goodness_of_fit
  CACCTTCATCCTAAAG A230212d28_rd256: goodness_of_fit
  CACGTTAGTGAGACTT A230212d28_rd256: goodness_of_fit
  CAGTAGCAAAACATGC A230212d28_rd256: goodness_of_fit
  CAGTGCCATCCATCCA A230212d28_rd256: goodness_of_fit
  CCAATAAAATACGATG A230212d28_rd256: goodness_of_fit
  CCAATCATGTATCACA A230212d28_rd256: goodness_of_fit
  CCCATATACTCACAGA A230212d28_rd256: goodness_of_fit
  CCGCTATCATTAACCC A230212d28_rd256: goodness_of_fit
  CCGGTTTTCGGGACCT A230212d28_rd256: goodness_of_fit
  CGATCTTTACGAAAAA A230212d28_rd256: goodness_of_fit
  CGCCGAACGGCGGCGC A230212d28_rd256: goodness_of_fit
  CGCGAACAACAGGGGA A230212d28_rd256: goodness_of_fit
  CGCGAATCACTAAGTA A230212d28_rd256: goodness_of_fit
  CGTATAACTGACGATT A230212d28_rd256: goodness_of_fit
  CTACACTACATCAAAT A230212d28_rd256: goodness_of_fit
  CTACTAGAGCAGCGAG A230212d28_rd256: goodness_of_fit
  CTATCTTAATCTACAG A230212d28_rd256: goodness_of_fit
  CTCATTACAGAAATTG A230212d28_rd256: goodness_of_fit
  CTGTGAAAAAAAATAC A230212d28_rd256: goodness_of_fit
  CTTCATCTCATTTAAA A230212d28_rd256: goodness_of_fit
  CTTGAATACACAAACA A230212d28_rd256: goodness_of_fit
  GAAAGAAAGCTATATG A230212d28_rd256: goodness_of_fit
  GACAGAAACAAAATTA A230212d28_rd256: goodness_of_fit
  GACCAAAGCTGCAGGG A230212d28_rd256: goodness_of_fit
  GAGATAGCCCAGAGGT A230212d28_rd256: goodness_of_fit
  GCAACGAGGTGTAACC A230212d28_rd256: goodness_of_fit
  GCATTATAATCTTGTG A230212d28_rd256: goodness_of_fit
  GCCGTAGCGAAATCTT A230212d28_rd256: goodness_of_fit
  GCTAATTCCAAAAGCG A230212d28_rd256: goodness_of_fit
  GCTGAAGACAGTATTA A230212d28_rd256: goodness_of_fit
  GCTTTTGAGAACCATT A230212d28_rd256: goodness_of_fit
  GGGTGCAATGAATCCA A230212d28_rd256: goodness_of_fit
  GGTTGCGTAGTTAATC A230212d28_rd256: goodness_of_fit
  GTAGAAACTAGGAGTT A230212d28_rd256: goodness_of_fit
  GTTATTATGACTTCAT A230212d28_rd256: goodness_of_fit
  GTTTTTCACTGAGTAG A230212d28_rd256: goodness_of_fit
  TACCATTTTGGTCCGC A230212d28_rd256: goodness_of_fit
  TACGAAAATCAAGAGC A230212d28_rd256: goodness_of_fit
  TACGACGGAAACAGAA A230212d28_rd256: goodness_of_fit
  TAGCTGGGCAAAGGCT A230212d28_rd256: goodness_of_fit
  TAGTTGCCCCGACCTG A230212d28_rd256: goodness_of_fit
  TATCGCAATATGATAA A230212d28_rd256: goodness_of_fit
  TATTATCTAAACGGCG A230212d28_rd256: goodness_of_fit
  TCAAACTATGATATTC A230212d28_rd256: goodness_of_fit
  TCAACCCTTCGATGTA A230212d28_rd256: goodness_of_fit
  TCAATGAATGCGGGGT A230212d28_rd256: goodness_of_fit
  TCCAAACAGCGTTAAA A230212d28_rd256: goodness_of_fit
  TCCCGAACTGAACGCG A230212d28_rd256: goodness_of_fit
  TCCTTTAACTAATCGA A230212d28_rd256: goodness_of_fit
  TCGCGGTAGATTTGCG A230212d28_rd256: goodness_of_fit
  TCGTCCTAGAACCTAA A230212d28_rd256: goodness_of_fit
  TCTTAACTACCCGATG A230212d28_rd256: goodness_of_fit
  TGAGATCAGCCGGGTG A230212d28_rd256: goodness_of_fit
  TGGCTAGCGCACACCA A230212d28_rd256: goodness_of_fit
  TTACGTCAATGTTTGA A230212d28_rd256: goodness_of_fit
  TTAGCAGTTAACGTAT A230212d28_rd256: goodness_of_fit
  TTAGTCATCTGGGTGC A230212d28_rd256: goodness_of_fit
  TTATAATGGCCGGTAT A230212d28_rd256: goodness_of_fit
  TTTACTCGACTAACCG A230212d28_rd256: goodness_of_fit
  TTTATGCCGATAGAGA A230212d28_rd256: goodness_of_fit
serum_replicates: {}